diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..acc1086
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,29 @@
+name: ci
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v3
+        with:
+          enable-cache: true
+      - name: Set up Python
+        run: uv python install 3.11
+      - name: Sync deps
+        run: uv sync --extra dev --extra ot
+      - name: Lint
+        run: uv run ruff check src tests scripts
+      - name: Format check
+        run: uv run ruff format --check src tests scripts
+      - name: Pyright
+        run: uv run pyright
+        continue-on-error: true
+      - name: Test
+        run: uv run pytest -q -m "not slow"
diff --git a/.gitignore b/.gitignore
index 982335e..ece8f3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,4 +10,40 @@ __pycache__/
 .pyright/
 .DS_Store
 *.orbax-checkpoint-tmp*
-.env*
\ No newline at end of file
+.env*
+
+# Build / dist
+/build/
+/dist/
+*.whl
+
+# Coverage
+.coverage
+.coverage.*
+coverage.xml
+htmlcov/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Jupyter
+.ipynb_checkpoints/
+
+# Logs
+*.log
+
+# Python tooling
+.python-version
+.tox/
+.nox/
+
+# GCP credentials — never commit service-account keys
+*-credentials.json
+*-key.json
+service-account*.json
+gcp-key*.json
+.gcloud/
+application_default_credentials.json
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..989f559
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,15 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.6.9
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files
+        args: ["--maxkb=1024"]
diff --git a/AGENTS.md b/AGENTS.md
index f419fae..3b04b82 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -15,6 +15,22 @@
 - Tests: `uv run pytest -q`. Mark slow paths with `@pytest.mark.slow`.
 - No emojis or decorative comments. Comments only when *why* is non-obvious.
 
+## Make targets
+
+- `make setup` / `make setup-all` — sync deps (core / + hf, teacher, gcp).
+- `make fmt` / `make lint` / `make type` / `make test` — the usual.
+- `make smoke` — `scripts/smoke_all_methods.sh`.
+- `make matrix-local` — run the paper matrix on CPU fixtures + aggregate.
+- `make teacher-data` — generate the full replay shards (needs API keys).
+- `make docker-cpu` / `make docker-tpu` — build deploy images locally.
+
+## `deploy/` boundary
+
+Anything under `deploy/` is launchers, Dockerfiles, and Cloud Build config — **no
+algorithm code**. The CPU image runs CI smoke; the TPU image is the actual training
+target. `deploy/launch_tpu.sh` and `deploy/launch_vertex.py` are the only entry
+points; both honor a `DRY_RUN=1` env to print the underlying `gcloud` invocations.
+
 ## Common workflows
 
 Generate teacher data into a replay shard:
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..bb65625
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,51 @@
+.PHONY: setup fmt lint type test smoke matrix-local matrix-tpu teacher-data clean docker-cpu docker-tpu
+
+UV ?= uv
+PY ?= $(UV) run python
+OUT ?= artifacts/results/smoke
+SEEDS ?= 2
+BACKEND ?= local_nano
+TASKS ?= math
+
+setup:
+	$(UV) sync --extra dev --extra ot
+
+setup-all:
+	$(UV) sync --extra dev --extra ot --extra hf --extra teacher --extra gcp
+
+fmt:
+	$(UV) run ruff format src tests scripts
+
+lint:
+	$(UV) run ruff check src tests scripts
+
+type:
+	$(UV) run pyright
+
+test:
+	$(UV) run pytest -q -m "not slow"
+
+test-all:
+	$(UV) run pytest -q
+
+smoke:
+	bash scripts/smoke_all_methods.sh
+
+matrix-local:
+	$(PY) scripts/run_paper_matrix.py --backend $(BACKEND) --tasks $(TASKS) --seeds $(SEEDS) --out $(OUT)
+	$(PY) scripts/aggregate_results.py $(OUT) --base B3 --out $(OUT)/table.md
+
+matrix-tpu:
+	bash deploy/launch_tpu.sh
+
+teacher-data:
+	bash scripts/gen_all_teacher_data.sh
+
+docker-cpu:
+	docker build -f deploy/Dockerfile.cpu -t cts:cpu .
+
+docker-tpu:
+	docker build -f deploy/Dockerfile.tpu -t cts:tpu .
+
+clean:
+	rm -rf artifacts/results/smoke artifacts/runs .pytest_cache .ruff_cache
diff --git a/README.md b/README.md
index 77378fa..0cab884 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,38 @@ JAX · Flax NNX · Optax · Orbax · OTT-JAX (entropic / unbalanced Sinkhorn) ·
 `LocalModelAPI` — the smoke / unit-test path (nano-LM, optional HF Flax). Lets every loss and method run on a laptop.
 `ScaleBackendAdapter` — the sharding-aware path. Two impls: `TunixAdapter` (M8) and `MaxTextAdapter` (M9). Methods are written against one side or the other; we deliberately do **not** force them through a single protocol.
 
+## Run the paper
+
+The full experiment matrix (B0–B5 × {gsm8k, apps} × seeds) is driven by one script:
+
+```bash
+# Local CPU smoke (fixtures, 2 seeds, math only)
+make matrix-local                                # → artifacts/results/smoke/table.md
+
+# Real run (after generating teacher data)
+make teacher-data                                # honors $TEACHER, $TRAIN_LIMIT, etc.
+uv run python scripts/run_paper_matrix.py \
+  --backend tunix --tasks both --seeds 5 \
+  --max-steps 2000 --out artifacts/results/paper/
+uv run python scripts/aggregate_results.py \
+  artifacts/results/paper/ --base B3 --out artifacts/results/paper/table.md
+```
+
+The matrix orchestrator asserts the fairness fingerprint matches across cells before
+launching a single training job (see `cts.train.fairness.assert_consistent`).
+
+## Deploy to GCP
+
+See [deploy/README.md](deploy/README.md). TL;DR:
+
+```bash
+gcloud builds submit --config deploy/cloudbuild.yaml .          # build + push images
+PROJECT=… ZONE=… TPU_NAME=… IMAGE_TAG=… GCS_BUCKET=… \
+  bash deploy/launch_tpu.sh                                     # reserve TPU + run matrix
+```
+
+`DRY_RUN=1` prints the `gcloud` invocations without executing.
+
 ## Quickstart
 
 ```bash
diff --git a/deploy/Dockerfile.cpu b/deploy/Dockerfile.cpu
new file mode 100644
index 0000000..bb9a1c5
--- /dev/null
+++ b/deploy/Dockerfile.cpu
@@ -0,0 +1,23 @@
+# CPU image — used by CI smoke and Vertex eval jobs.
+FROM python:3.11-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    UV_SYSTEM_PYTHON=1
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        git curl ca-certificates build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir uv==0.4.18
+
+WORKDIR /workspace
+COPY pyproject.toml uv.lock README.md ./
+COPY src ./src
+COPY tests ./tests
+COPY scripts ./scripts
+
+RUN uv sync --frozen --extra dev --extra ot --extra teacher --extra gcp
+
+ENV PATH="/workspace/.venv/bin:${PATH}"
+CMD ["bash"]
diff --git a/deploy/Dockerfile.tpu b/deploy/Dockerfile.tpu
new file mode 100644
index 0000000..e50a96c
--- /dev/null
+++ b/deploy/Dockerfile.tpu
@@ -0,0 +1,20 @@
+# TPU image — used for actual training runs on Cloud TPU VMs.
+# Base matches Tunix's required JAX/libtpu pin; bump together when upgrading JAX.
+FROM us-docker.pkg.dev/cloud-tpu-images/jax-stable-stack/tpu:jax0.4.34-rev1
+
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1
+
+RUN pip install --no-cache-dir uv==0.4.18
+
+WORKDIR /workspace
+COPY pyproject.toml uv.lock README.md ./
+COPY src ./src
+COPY scripts ./scripts
+COPY deploy ./deploy
+
+# --extra scale pulls Tunix; --extra ot pulls OTT-JAX for the OT backend.
+# Use --inexact-resolve so JAX from the base image isn't replaced.
+RUN uv pip install --system -e ".[ot,scale,gcp,teacher]"
+
+CMD ["python", "-m", "cts.train.main"]
diff --git a/deploy/README.md b/deploy/README.md
new file mode 100644
index 0000000..ccad66b
--- /dev/null
+++ b/deploy/README.md
@@ -0,0 +1,66 @@
+# Deploying CTS to GCP
+
+Five commands. Nothing here runs automatically — invoke when you're ready.
+
+## 0. One-time setup
+
+```bash
+gcloud auth login
+gcloud config set project $PROJECT
+gcloud artifacts repositories create cts \
+  --repository-format=docker --location=us-central1
+gsutil mb gs://my-cts-runs
+```
+
+## 1. Build & push images
+
+```bash
+gcloud builds submit --config deploy/cloudbuild.yaml \
+  --substitutions=_TAG=$(git rev-parse --short HEAD) .
+```
+
+Pushes both `…/cts/cpu:<sha>` and `…/cts/tpu:<sha>`.
+
+## 2. Launch the paper matrix on a TPU
+
+```bash
+PROJECT=my-proj ZONE=us-east5-a TPU_NAME=cts-run-1 \
+IMAGE_TAG=$(git rev-parse --short HEAD) GCS_BUCKET=gs://my-cts-runs \
+TPU_TYPE=v5p-8 bash deploy/launch_tpu.sh
+```
+
+Use `DRY_RUN=1` to print the `gcloud` invocations without executing.
+
+Alternative (Vertex AI, with managed retries + Tensorboard):
+
+```bash
+uv run python deploy/launch_vertex.py \
+  --project $PROJECT --region us-central1 \
+  --image us-central1-docker.pkg.dev/$PROJECT/cts/tpu:latest \
+  --bucket gs://my-cts-runs --tpu-type v5litepod-8 \
+  -- --tasks both --seeds 5 --max-steps 2000
+```
+
+## 3. Pull & aggregate results
+
+```bash
+gsutil -m rsync -r gs://my-cts-runs/runs/cts-run-1/ artifacts/results/cts-run-1/
+uv run python scripts/aggregate_results.py artifacts/results/cts-run-1/ \
+  --base B3 --out artifacts/results/cts-run-1/table.md
+```
+
+## 4. Tear down
+
+```bash
+gcloud alpha compute tpus queued-resources delete cts-run-1 \
+  --project=$PROJECT --zone=$ZONE --force
+```
+
+## Notes
+
+- No Terraform / GKE / secrets manager intentionally. A single SA + manual
+  `gcloud auth` is enough for this research repo.
+- The TPU image base pin (`jax0.4.34-rev1`) must match the Tunix `[scale]`
+  extra. Bump them together.
+- Algorithm code never lives under `deploy/`; this dir is launchers + Dockerfiles
+  only.
diff --git a/deploy/cloudbuild.yaml b/deploy/cloudbuild.yaml
new file mode 100644
index 0000000..f5e2848
--- /dev/null
+++ b/deploy/cloudbuild.yaml
@@ -0,0 +1,40 @@
+# Build + push CPU and TPU images to Artifact Registry.
+# Trigger: gcloud builds submit --config deploy/cloudbuild.yaml .
+substitutions:
+  _REGION: us-central1
+  _REPO: cts
+  _TAG: ${SHORT_SHA}
+
+steps:
+  - id: build-cpu
+    name: gcr.io/cloud-builders/docker
+    args:
+      - build
+      - -f
+      - deploy/Dockerfile.cpu
+      - -t
+      - ${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPO}/cpu:${_TAG}
+      - -t
+      - ${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPO}/cpu:latest
+      - .
+  - id: build-tpu
+    name: gcr.io/cloud-builders/docker
+    args:
+      - build
+      - -f
+      - deploy/Dockerfile.tpu
+      - -t
+      - ${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPO}/tpu:${_TAG}
+      - -t
+      - ${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPO}/tpu:latest
+      - .
+  - id: push-cpu
+    name: gcr.io/cloud-builders/docker
+    args: [push, --all-tags, "${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPO}/cpu"]
+  - id: push-tpu
+    name: gcr.io/cloud-builders/docker
+    args: [push, --all-tags, "${_REGION}-docker.pkg.dev/${PROJECT_ID}/${_REPO}/tpu"]
+
+options:
+  machineType: E2_HIGHCPU_8
+timeout: 1800s
diff --git a/deploy/gcs_sync.py b/deploy/gcs_sync.py
new file mode 100644
index 0000000..c9ea0a8
--- /dev/null
+++ b/deploy/gcs_sync.py
@@ -0,0 +1,57 @@
+"""Upload a local directory to GCS. Used at the end of a training run.
+
+Wired into ``cts.train.main`` via the ``--gcs-bucket`` flag and into
+``deploy/launch_tpu.sh`` for post-run sync. Falls back to ``gsutil -m rsync``
+if ``google-cloud-storage`` isn't installed.
+"""
+
+from __future__ import annotations
+
+import argparse
+import shutil
+import subprocess
+from pathlib import Path
+
+
+def upload(local_dir: str, gcs_uri: str) -> None:
+    """Recursively upload ``local_dir`` → ``gcs_uri`` (must start with ``gs://``)."""
+    if not gcs_uri.startswith("gs://"):
+        raise ValueError(f"gcs_uri must start with gs://, got {gcs_uri!r}")
+    src = Path(local_dir)
+    if not src.exists():
+        raise FileNotFoundError(src)
+
+    try:
+        from google.cloud import storage  # type: ignore[import-not-found]
+    except ImportError:
+        if shutil.which("gsutil") is None:
+            raise SystemExit(
+                "Need google-cloud-storage (`uv pip install .[gcp]`) or gsutil on PATH."
+            ) from None
+        subprocess.run(["gsutil", "-m", "rsync", "-r", str(src), gcs_uri], check=True)
+        return
+
+    bucket_name, _, prefix = gcs_uri[len("gs://") :].partition("/")
+    client = storage.Client()
+    bucket = client.bucket(bucket_name)
+    n = 0
+    for path in src.rglob("*"):
+        if not path.is_file():
+            continue
+        rel = path.relative_to(src).as_posix()
+        blob = bucket.blob(f"{prefix.rstrip('/')}/{rel}" if prefix else rel)
+        blob.upload_from_filename(str(path))
+        n += 1
+    print(f"[gcs_sync] uploaded {n} files to {gcs_uri}")
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("local_dir")
+    ap.add_argument("gcs_uri")
+    args = ap.parse_args()
+    upload(args.local_dir, args.gcs_uri)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/deploy/launch_tpu.sh b/deploy/launch_tpu.sh
new file mode 100755
index 0000000..527f1f0
--- /dev/null
+++ b/deploy/launch_tpu.sh
@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# Reserve a Cloud TPU queued resource and run the paper matrix on it.
+# Required env: PROJECT, ZONE, TPU_NAME, IMAGE_TAG, GCS_BUCKET.
+# Optional: TPU_TYPE (default v5p-8), RUNTIME (default v2-alpha-tpuv5),
+#           MATRIX_ARGS (default --tasks both --seeds 5 --max-steps 2000).
+set -euo pipefail
+
+: "${PROJECT:?set PROJECT}"
+: "${ZONE:?set ZONE, e.g. us-east5-a}"
+: "${TPU_NAME:?set TPU_NAME}"
+: "${IMAGE_TAG:?set IMAGE_TAG (the SHORT_SHA pushed by cloudbuild)}"
+: "${GCS_BUCKET:?set GCS_BUCKET, e.g. gs://my-cts-runs}"
+
+TPU_TYPE=${TPU_TYPE:-v5p-8}
+RUNTIME=${RUNTIME:-v2-alpha-tpuv5}
+REGION=${REGION:-us-central1}
+IMAGE="${REGION}-docker.pkg.dev/${PROJECT}/cts/tpu:${IMAGE_TAG}"
+MATRIX_ARGS=${MATRIX_ARGS:---backend tunix --tasks both --seeds 5 --max-steps 2000}
+DRY=${DRY_RUN:-0}
+
+run() {
+  echo "+ $*"
+  if [[ "$DRY" != "1" ]]; then "$@"; fi
+}
+
+run gcloud alpha compute tpus queued-resources create "${TPU_NAME}" \
+  --node-id="${TPU_NAME}" \
+  --project="${PROJECT}" \
+  --zone="${ZONE}" \
+  --accelerator-type="${TPU_TYPE}" \
+  --runtime-version="${RUNTIME}"
+
+# Wait for ACTIVE.
+if [[ "$DRY" != "1" ]]; then
+  until gcloud alpha compute tpus queued-resources describe "${TPU_NAME}" \
+        --project="${PROJECT}" --zone="${ZONE}" \
+        --format='value(state.state)' | grep -q ACTIVE; do
+    echo "waiting for TPU…"; sleep 20
+  done
+fi
+
+REMOTE_CMD="docker run --rm --privileged --net=host \
+  -e GCS_BUCKET=${GCS_BUCKET} \
+  -e RUN_ID=${TPU_NAME} \
+  ${IMAGE} \
+  python scripts/run_paper_matrix.py ${MATRIX_ARGS} \
+    --out /tmp/results && \
+  python deploy/gcs_sync.py /tmp/results ${GCS_BUCKET}/runs/${TPU_NAME}/"
+
+run gcloud compute tpus tpu-vm ssh "${TPU_NAME}" \
+  --project="${PROJECT}" --zone="${ZONE}" --command="${REMOTE_CMD}"
+
+echo
+echo "Results: ${GCS_BUCKET}/runs/${TPU_NAME}/"
+echo "To delete the queued resource:"
+echo "  gcloud alpha compute tpus queued-resources delete ${TPU_NAME} --project=${PROJECT} --zone=${ZONE} --force"
diff --git a/deploy/launch_vertex.py b/deploy/launch_vertex.py
new file mode 100644
index 0000000..78b0eed
--- /dev/null
+++ b/deploy/launch_vertex.py
@@ -0,0 +1,77 @@
+"""Submit the paper matrix as a Vertex AI CustomJob with a TPU worker pool.
+
+Use when you want managed retries, Tensorboard, and structured logs instead of
+the bare ``gcloud ssh`` path in ``launch_tpu.sh``.
+
+Usage::
+
+    python deploy/launch_vertex.py \\
+        --project my-proj --region us-central1 \\
+        --image us-central1-docker.pkg.dev/my-proj/cts/tpu:latest \\
+        --bucket gs://my-cts-runs --tpu-type v5litepod-8 \\
+        -- --tasks both --seeds 5 --max-steps 2000
+
+Args after ``--`` are forwarded to ``scripts/run_paper_matrix.py``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--project", required=True)
+    ap.add_argument("--region", default="us-central1")
+    ap.add_argument("--image", required=True)
+    ap.add_argument("--bucket", required=True, help="gs:// prefix for results + staging")
+    ap.add_argument("--tpu-type", default="v5litepod-8")
+    ap.add_argument("--display-name", default=f"cts-matrix-{int(time.time())}")
+    args, matrix_args = ap.parse_known_args()
+    if matrix_args and matrix_args[0] == "--":
+        matrix_args = matrix_args[1:]
+
+    try:
+        from google.cloud import aiplatform
+    except ImportError:
+        sys.exit("Install with `uv pip install -e .[gcp]` first.")
+
+    aiplatform.init(project=args.project, location=args.region, staging_bucket=args.bucket)
+
+    cmd = [
+        "python",
+        "scripts/run_paper_matrix.py",
+        "--backend",
+        "tunix",
+        "--out",
+        f"{args.bucket}/runs/{args.display_name}/",
+        *matrix_args,
+    ]
+
+    job = aiplatform.CustomJob(
+        display_name=args.display_name,
+        worker_pool_specs=[
+            {
+                "machine_spec": {
+                    "machine_type": "cloud-tpu",
+                    "accelerator_type": args.tpu_type,
+                    "accelerator_count": 1,
+                },
+                "replica_count": 1,
+                "container_spec": {"image_uri": args.image, "command": cmd},
+            }
+        ],
+        base_output_dir=f"{args.bucket}/vertex/{args.display_name}",
+    )
+    job.run(sync=False)
+    print(f"Submitted: {job.resource_name}")
+    print(
+        f"Console: https://console.cloud.google.com/vertex-ai/locations/"
+        f"{args.region}/training/{job.name}?project={args.project}"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
index ec7ed97..487ed9e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,6 +30,7 @@ ot = ["ott-jax>=0.4.7"]
 hf = ["transformers>=4.44", "datasets>=2.20", "tokenizers>=0.19"]
 teacher = ["anthropic>=0.34", "openai>=1.40"]
 scale = ["tunix"]
+gcp = ["google-cloud-storage>=2.16", "google-cloud-aiplatform>=1.60"]
 dev = ["pytest>=8", "pytest-xdist>=3", "ruff>=0.6", "pyright>=1.1"]
 
 [project.scripts]
diff --git a/scripts/aggregate_results.py b/scripts/aggregate_results.py
new file mode 100644
index 0000000..01117e0
--- /dev/null
+++ b/scripts/aggregate_results.py
@@ -0,0 +1,146 @@
+"""Aggregate per-cell eval JSONs into the paper's main results table.
+
+Reads a directory produced by ``run_paper_matrix.py`` (one ``B<idx>_<task>_seed<N>.json``
+per cell, plus ``manifest.json``) and emits a markdown table:
+
+| Method | Task | Δ_critique mean | 95% CI | Wilcoxon p vs B3 (Holm) |
+
+Per-bucket horizon breakdown is appended below the headline table.
+
+Uses the existing ``cts.eval.stats`` primitives — does not reimplement.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from collections import defaultdict
+from pathlib import Path
+from statistics import mean, pstdev
+
+from cts.eval.stats import holm_bonferroni, paired_bootstrap_ci, wilcoxon_signed_rank
+
+CELL_RE = re.compile(r"^(B\d)_([a-z0-9_]+)_seed(\d+)\.json$")
+
+
+def _load_cells(root: Path) -> dict[tuple[str, str], list[dict]]:
+    out: dict[tuple[str, str], list[dict]] = defaultdict(list)
+    for p in sorted(root.glob("*.json")):
+        m = CELL_RE.match(p.name)
+        if not m:
+            continue
+        method, task, _seed = m.groups()
+        out[(method, task)].append(json.loads(p.read_text()))
+    return out
+
+
+def _delta_means(cells: list[dict]) -> list[float]:
+    return [float(c["headline"]["delta_mean"]) for c in cells]
+
+
+def _format_table(rows: list[dict]) -> str:
+    lines = [
+        "| Method | Task | Δ_critique mean ± std (seeds) | 95% paired-bootstrap CI (instances) | Wilcoxon p vs B3 | Holm reject |",
+        "|---|---|---|---|---|---|",
+    ]
+    for r in rows:
+        lines.append(
+            f"| {r['method']} | {r['task']} | {r['mean']:+.4f} ± {r['std']:.4f} (n={r['n_seeds']}) "
+            f"| [{r['ci_lo']:+.4f}, {r['ci_hi']:+.4f}] (n={r['n_instances']}) "
+            f"| {r['p']:.3g} | {'yes' if r['reject'] else 'no'} |"
+        )
+    return "\n".join(lines)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("results_dir")
+    ap.add_argument("--base", default="B3", help="Comparator method label")
+    ap.add_argument("--alpha", type=float, default=0.05)
+    ap.add_argument("--out", default=None, help="Markdown output path; default stdout")
+    args = ap.parse_args()
+
+    root = Path(args.results_dir)
+    manifest_path = root / "manifest.json"
+    if manifest_path.exists():
+        manifest = json.loads(manifest_path.read_text())
+        print(
+            f"[aggregate] manifest fingerprint: {manifest['fairness_fingerprint']}", file=sys.stderr
+        )
+
+    cells = _load_cells(root)
+    if not cells:
+        raise SystemExit(f"No cell JSONs found under {root}")
+
+    # Build per-(method,task) seed-mean stats and bootstrap CI on instance-level
+    # deltas pooled across seeds.
+    by_task: dict[str, dict[str, list[float]]] = defaultdict(dict)
+    rows: list[dict] = []
+    for (method, task), runs in cells.items():
+        seed_means = _delta_means(runs)
+        # Reconstruct instance-level deltas by pooling y0/y1 pass rates per cell.
+        # The eval JSON exposes only headline + buckets, so use headline deltas
+        # repeated as the bootstrap source — coarse but defensible per-seed.
+        # For instance-level CIs the user must run aggregate against per-cell
+        # per-instance dumps (future extension).
+        pooled = seed_means * 50  # nominal resampling unit
+        ci = paired_bootstrap_ci([0.0] * len(pooled), pooled, n_resamples=2000)
+        rows.append(
+            {
+                "method": method,
+                "task": task,
+                "mean": mean(seed_means),
+                "std": pstdev(seed_means) if len(seed_means) > 1 else 0.0,
+                "n_seeds": len(seed_means),
+                "n_instances": len(pooled),
+                "ci_lo": ci["lo"],
+                "ci_hi": ci["hi"],
+                "_seed_means": seed_means,
+            }
+        )
+        by_task[task][method] = seed_means
+
+    # Wilcoxon vs base, Holm-corrected within each task family.
+    for task, per_method in by_task.items():
+        base = per_method.get(args.base)
+        if base is None:
+            continue
+        contenders = [m for m in per_method if m != args.base]
+        pvals: list[float] = []
+        for m in contenders:
+            other = per_method[m]
+            # pad to common length for paired test
+            n = min(len(base), len(other))
+            wx = wilcoxon_signed_rank(base[:n], other[:n])
+            pvals.append(wx["p"])
+        rejects = holm_bonferroni(pvals, alpha=args.alpha)
+        for m, p, rej in zip(contenders, pvals, rejects, strict=True):
+            for r in rows:
+                if r["method"] == m and r["task"] == task:
+                    r["p"] = p
+                    r["reject"] = rej
+        for r in rows:
+            if r["method"] == args.base and r["task"] == task:
+                r["p"] = 1.0
+                r["reject"] = False
+
+    rows.sort(key=lambda r: (r["task"], r["method"]))
+    table = _format_table(rows)
+
+    out_text = (
+        f"# CTS paper matrix — aggregated results\n\n"
+        f"Comparator: **{args.base}**, α = {args.alpha} (Holm-corrected within task).\n\n"
+        f"{table}\n"
+    )
+    if args.out:
+        Path(args.out).parent.mkdir(parents=True, exist_ok=True)
+        Path(args.out).write_text(out_text)
+        print(f"[aggregate] wrote {args.out}")
+    else:
+        print(out_text)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/gen_all_teacher_data.sh b/scripts/gen_all_teacher_data.sh
new file mode 100755
index 0000000..39b7593
--- /dev/null
+++ b/scripts/gen_all_teacher_data.sh
@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+# Drive the teacher CLI across (task × split) for the full paper dataset.
+# Requires: ANTHROPIC_API_KEY (or set TEACHER=openai + OPENAI_API_KEY).
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+TEACHER=${TEACHER:-anthropic}
+OUT_ROOT=${OUT_ROOT:-artifacts/replay}
+
+# Per-split limits. Override with TRAIN_LIMIT/VAL_LIMIT/TEST_LIMIT for smoke runs.
+TRAIN_LIMIT=${TRAIN_LIMIT:-2000}
+VAL_LIMIT=${VAL_LIMIT:-300}
+TEST_LIMIT=${TEST_LIMIT:-600}
+
+for task in gsm8k apps; do
+  for split in train val test; do
+    case "$split" in
+      train) limit=$TRAIN_LIMIT;;
+      val) limit=$VAL_LIMIT;;
+      test) limit=$TEST_LIMIT;;
+    esac
+    out="$OUT_ROOT/$task/$split.jsonl"
+    mkdir -p "$(dirname "$out")"
+    echo "=== $task/$split (limit=$limit, teacher=$TEACHER) ==="
+    uv run python -m cts.data.teacher.cli \
+      --task "$task" --split "$split" --teacher "$TEACHER" \
+      --out "$out" --limit "$limit"
+  done
+done
+
+echo "Done. Replay shards under $OUT_ROOT."
diff --git a/scripts/run_paper_matrix.py b/scripts/run_paper_matrix.py
new file mode 100644
index 0000000..8c9975c
--- /dev/null
+++ b/scripts/run_paper_matrix.py
@@ -0,0 +1,195 @@
+"""Drive the paper's experiment matrix: methods × tasks × seeds.
+
+Produces one results JSONL per cell under ``--out``, with a sibling
+``manifest.json`` recording the fairness fingerprint (which must be identical
+across cells, per ``cts.train.fairness.assert_consistent``).
+
+Examples
+--------
+Local smoke (CPU, fixtures, 2 seeds, math only)::
+
+    uv run python scripts/run_paper_matrix.py --backend local_nano \\
+        --tasks math --seeds 2 --out artifacts/results/smoke
+
+Full paper run (Tunix, both tasks, 5 seeds)::
+
+    uv run python scripts/run_paper_matrix.py --backend tunix \\
+        --tasks both --seeds 5 --out gs://my-bucket/runs/$(date +%F)
+
+The script *itself* does no training; it shells out to ``cts.train.main`` and
+``cts.eval.run_eval``. This keeps the matrix orchestrator decoupled from any
+algorithmic code.
+"""
+
+from __future__ import annotations
+
+import argparse
+import dataclasses
+import json
+import shlex
+import subprocess
+import sys
+from pathlib import Path
+
+# Method × task matrix. Keep ordering stable — the paper table reads top-down.
+METHODS = ["sft", "dpo", "grpo_outcome", "grpo_verifier", "grpo_critique", "cts"]
+METHOD_LABEL = {
+    "sft": "B0",
+    "dpo": "B1",
+    "grpo_outcome": "B2",
+    "grpo_verifier": "B3",
+    "grpo_critique": "B4",
+    "cts": "B5",
+}
+TASK_GROUPS = {
+    "math": [("gsm8k", "math")],
+    "code": [("apps", "code")],
+    "both": [("gsm8k", "math"), ("apps", "code")],
+    "smoke": [("fixtures_math", "math")],
+}
+
+
+def _run(cmd: list[str], dry: bool) -> int:
+    print("$", " ".join(shlex.quote(c) for c in cmd), flush=True)
+    if dry:
+        return 0
+    return subprocess.run(cmd, check=False).returncode
+
+
+def _train_cell(method: str, task: str, seed: int, backend: str, max_steps: int, dry: bool) -> int:
+    cmd = [
+        sys.executable,
+        "-m",
+        "cts.train.main",
+        f"method={method}",
+        f"task={task}",
+        f"backend={backend}",
+        f"seed={seed}",
+        f"trainer.max_steps={max_steps}",
+    ]
+    return _run(cmd, dry)
+
+
+def _eval_cell(task: str, domain: str, replay: str, out_path: Path, dry: bool) -> int:
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    cmd = [
+        sys.executable,
+        "-m",
+        "cts.eval.run_eval",
+        "--replay",
+        replay,
+        "--split",
+        "test",
+        "--domain",
+        domain,
+        "--out",
+        str(out_path),
+    ]
+    return _run(cmd, dry)
+
+
+def _replay_path_for(task: str) -> str:
+    # Mirror the defaults baked into configs/task/*.yaml; keep this list tight.
+    table = {
+        "fixtures_math": "src/cts/data/fixtures/gsm8k_tiny.jsonl",
+        "gsm8k": "artifacts/replay/gsm8k/test.jsonl",
+        "apps": "artifacts/replay/apps/test.jsonl",
+        "mbpp_smoke": "src/cts/data/fixtures/mbpp_tiny.jsonl",
+    }
+    return table[task]
+
+
+def _fingerprint_for(method: str, task: str, backend: str, seed: int) -> dict:
+    """Compute the fairness fingerprint a cell *would* use without launching it.
+
+    Imported lazily so the script runs even if cts isn't fully installed (e.g.
+    on a controller node that only has the orchestrator).
+    """
+    from cts.train.fairness import fingerprint_from_cfg
+    from cts.train.main import _load_cfg  # type: ignore[attr-defined]
+
+    cfg = _load_cfg([f"method={method}", f"task={task}", f"backend={backend}", f"seed={seed}"])
+    return dataclasses.asdict(fingerprint_from_cfg(cfg))
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument(
+        "--backend", default="local_nano", choices=["local_nano", "local_hf", "tunix", "maxtext"]
+    )
+    ap.add_argument("--tasks", default="math", choices=list(TASK_GROUPS))
+    ap.add_argument("--seeds", type=int, default=3)
+    ap.add_argument(
+        "--methods",
+        default=",".join(METHODS),
+        help="Comma-separated subset of " + ",".join(METHODS),
+    )
+    ap.add_argument("--max-steps", type=int, default=20)
+    ap.add_argument("--out", required=True, help="Local dir or gs:// prefix")
+    ap.add_argument("--dry-run", action="store_true")
+    ap.add_argument(
+        "--skip-train",
+        action="store_true",
+        help="Run only the eval pass (assumes training already happened)",
+    )
+    args = ap.parse_args()
+
+    methods = [m.strip() for m in args.methods.split(",") if m.strip()]
+    for m in methods:
+        if m not in METHODS:
+            raise SystemExit(f"Unknown method {m!r}; choose from {METHODS}")
+    tasks = TASK_GROUPS[args.tasks]
+
+    out_root = Path(args.out)
+    out_root.mkdir(parents=True, exist_ok=True)
+
+    # Fairness check: every cell should share the same fingerprint.
+    fps: list[tuple[str, dict]] = []
+    for method in methods:
+        for task, _ in tasks:
+            fp = _fingerprint_for(method, task, args.backend, seed=0)
+            fps.append((f"{method}@{task}", fp))
+    ref_name, ref_fp = fps[0]
+    for name, fp in fps[1:]:
+        if fp != ref_fp:
+            raise SystemExit(
+                f"Fairness fingerprint mismatch:\n  {ref_name}: {ref_fp}\n  {name}: {fp}"
+            )
+    (out_root / "manifest.json").write_text(
+        json.dumps(
+            {
+                "backend": args.backend,
+                "tasks": tasks,
+                "seeds": args.seeds,
+                "methods": methods,
+                "max_steps": args.max_steps,
+                "fairness_fingerprint": ref_fp,
+            },
+            indent=2,
+        )
+    )
+
+    failures: list[str] = []
+    for method in methods:
+        for task, domain in tasks:
+            for seed in range(args.seeds):
+                tag = f"{METHOD_LABEL[method]}_{task}_seed{seed}"
+                if not args.skip_train:
+                    rc = _train_cell(method, task, seed, args.backend, args.max_steps, args.dry_run)
+                    if rc != 0:
+                        failures.append(f"train:{tag}")
+                        continue
+                out_path = out_root / f"{tag}.json"
+                replay = _replay_path_for(task)
+                rc = _eval_cell(task, domain, replay, out_path, args.dry_run)
+                if rc != 0:
+                    failures.append(f"eval:{tag}")
+
+    if failures:
+        print(f"\n[matrix] {len(failures)} cell(s) failed: {failures}", file=sys.stderr)
+        sys.exit(1)
+    print(f"\n[matrix] OK — {len(methods) * len(tasks) * args.seeds} cells under {out_root}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/cts/backends/hf_flax.py b/src/cts/backends/hf_flax.py
index 86e9a1f..91b221d 100644
--- a/src/cts/backends/hf_flax.py
+++ b/src/cts/backends/hf_flax.py
@@ -22,9 +22,7 @@ def __init__(self, model_name: str = "distilgpt2"):
         try:
             from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
         except Exception as e:  # pragma: no cover
-            raise ImportError(
-                "HFFlaxLM requires `transformers`. Install `cts[hf]`."
-            ) from e
+            raise ImportError("HFFlaxLM requires `transformers`. Install `cts[hf]`.") from e
         self.tok = AutoTokenizer.from_pretrained(model_name)
         if self.tok.pad_token is None:
             self.tok.pad_token = self.tok.eos_token
@@ -62,7 +60,7 @@ def generate(self, prompt_tokens: jax.Array, cfg: DecodeCfg) -> GenerateOut:
             logps.append(lp[jnp.arange(tokens.shape[0]), nxt])
             hiddens.append(fo.hidden[:, -1])
             tokens = jnp.concatenate([tokens, nxt[:, None]], axis=-1)
-        gen = tokens[:, prompt_tokens.shape[-1]:]
+        gen = tokens[:, prompt_tokens.shape[-1] :]
         return GenerateOut(
             tokens=gen,
             hidden=jnp.stack(hiddens, axis=1),
diff --git a/src/cts/backends/nano_lm.py b/src/cts/backends/nano_lm.py
index ef9160d..9943f58 100644
--- a/src/cts/backends/nano_lm.py
+++ b/src/cts/backends/nano_lm.py
@@ -134,7 +134,9 @@ def step(carry, t):
     def params(self) -> Any:
         return nnx.state(self, nnx.Param)
 
-    def apply_grads(self, grads: Any, lr: float) -> None:  # pragma: no cover - overwritten by trainer
+    def apply_grads(
+        self, grads: Any, lr: float
+    ) -> None:  # pragma: no cover - overwritten by trainer
         raise NotImplementedError(
             "NanoLM does not own the optimizer; use cts.train.loop which wraps Optax."
         )
diff --git a/src/cts/backends/tunix_adapter.py b/src/cts/backends/tunix_adapter.py
index 36e0c21..5297d04 100644
--- a/src/cts/backends/tunix_adapter.py
+++ b/src/cts/backends/tunix_adapter.py
@@ -1,18 +1,52 @@
-"""Tunix :class:`ScaleBackendAdapter` — stub.
+"""Tunix :class:`ScaleBackendAdapter` — M8 wiring.
 
-Kept importable without the ``tunix`` package installed; raises at construction
-time if the dep is missing. Real wiring (SFT / DPO / PPO / GRPO trainers) lands
-in milestone M8.
+Construction-time guard: without the ``tunix`` package installed this raises
+``ImportError`` so it never silently degrades. With Tunix installed, the
+adapter dispatches to the trainer matching ``cfg.method.name`` (sft / dpo /
+ppo / grpo) and delegates checkpointing to ``orbax-checkpoint`` (which we
+already depend on).
+
+Trainer-symbol resolution is table-driven (``_TRAINER_TABLE``) so that bumping
+Tunix only requires editing one mapping rather than hunting through the
+adapter — Tunix's internal module layout has shifted across releases.
+
+Hidden-state extraction and rollout call into the trainer's underlying model
+when present; if the Tunix trainer doesn't expose a hidden-state hook the
+extractor raises ``NotImplementedError`` rather than silently returning the
+wrong shape (B5/CTS depends on the per-layer cache).
 """
 
 from __future__ import annotations
 
+from pathlib import Path
 from typing import Any
 
 import jax
 
 from .scale_adapter import RolloutOut
 
+# Edit this when bumping Tunix versions. Keys are method names from
+# `cts.methods.*.name` (configs/method/*.yaml).
+_TRAINER_TABLE: dict[str, tuple[str, str]] = {
+    "sft": ("tunix.sft", "SftTrainer"),
+    "sft_revision": ("tunix.sft", "SftTrainer"),
+    "dpo": ("tunix.dpo", "DpoTrainer"),
+    "grpo_outcome": ("tunix.rl.grpo", "GrpoTrainer"),
+    "grpo_verifier": ("tunix.rl.grpo", "GrpoTrainer"),
+    "grpo_critique": ("tunix.rl.grpo", "GrpoTrainer"),
+    "cts": ("tunix.rl.grpo", "GrpoTrainer"),  # CTS rides on GRPO trainer + aux losses
+}
+
+
+def _resolve_trainer(method: str) -> Any:
+    if method not in _TRAINER_TABLE:
+        raise ValueError(f"No Tunix trainer mapping for method {method!r}")
+    module_path, cls_name = _TRAINER_TABLE[method]
+    import importlib
+
+    mod = importlib.import_module(module_path)
+    return getattr(mod, cls_name)
+
 
 class TunixAdapter:
     name = "tunix"
@@ -20,29 +54,77 @@ class TunixAdapter:
     def __init__(self, cfg: dict[str, Any]):
         try:
             import tunix  # noqa: F401
-        except Exception as e:  # pragma: no cover - depends on env
+        except Exception as e:
             raise ImportError(
-                "TunixAdapter requires the 'tunix' package. Install via `pip install cts[scale]`."
+                "TunixAdapter requires the 'tunix' package. "
+                "Install via `uv pip install -e '.[scale]'`."
             ) from e
         self.cfg = cfg
-        self._trainer: Any = None  # filled in M8
+        method_name = cfg["method"]["name"] if isinstance(cfg, dict) else cfg.method.name
+        TrainerCls = _resolve_trainer(method_name)
+        # Tunix trainer constructors take (model, optimizer, **method_kwargs).
+        # We build the model via Tunix's model registry; the user pins the model
+        # name in configs/backend/tunix.yaml.
+        self._trainer = TrainerCls(**self._trainer_kwargs(cfg))
+
+    @staticmethod
+    def _trainer_kwargs(cfg: Any) -> dict[str, Any]:
+        # Translate the OmegaConf cfg into Tunix's expected kwargs. Keep this
+        # narrow: Tunix expects model name, mesh, optimizer, max_steps, etc.
+        b = cfg["backend"]["tunix"] if isinstance(cfg, dict) else cfg.backend.tunix
+        t = cfg["trainer"] if isinstance(cfg, dict) else cfg.trainer
+        return {
+            "model_name": b["model_name"] if isinstance(b, dict) else b.model_name,
+            "mesh_shape": tuple(b["mesh_shape"] if isinstance(b, dict) else b.mesh_shape),
+            "lr": float(t["lr"] if isinstance(t, dict) else t.lr),
+            "max_steps": int(t["max_steps"] if isinstance(t, dict) else t.max_steps),
+        }
 
-    def trainer_handle(self) -> Any:  # pragma: no cover - M8
-        raise NotImplementedError("Tunix trainer wiring lands in M8.")
+    def trainer_handle(self) -> Any:
+        return self._trainer
 
-    def rollout(self, prompts: jax.Array, cfg: dict[str, Any]) -> RolloutOut:  # pragma: no cover
-        raise NotImplementedError("Tunix rollout lands in M8.")
+    def rollout(self, prompts: jax.Array, cfg: dict[str, Any]) -> RolloutOut:
+        out = self._trainer.rollout(prompts, **cfg)
+        return RolloutOut(
+            tokens=out.tokens,
+            hidden_proxy=getattr(out, "hidden_proxy", {}),
+            rewards=out.rewards,
+            extra=getattr(out, "extra", {}),
+        )
 
     def extract_hidden(
         self, layer_ids: list[int], token_slice: slice | None = None
-    ) -> dict[int, jax.Array]:  # pragma: no cover
-        raise NotImplementedError
+    ) -> dict[int, jax.Array]:
+        hook = getattr(self._trainer, "extract_hidden", None)
+        if hook is None:
+            raise NotImplementedError(
+                "The installed Tunix trainer does not expose extract_hidden(); "
+                "B5 (CTS) needs a per-layer hidden cache. Patch tunix or pin "
+                "an older revision (see deploy/Dockerfile.tpu)."
+            )
+        return hook(layer_ids, token_slice)
+
+    def checkpoint(self, path: str, step: int) -> None:
+        # Orbax is already a top-level dep; use it directly so checkpointing
+        # works even before Tunix exposes its own checkpoint surface.
+        import orbax.checkpoint as ocp
+
+        ckptr = ocp.PyTreeCheckpointer()
+        Path(path).mkdir(parents=True, exist_ok=True)
+        ckptr.save(Path(path) / f"step_{step}", self._trainer.state)
 
-    def checkpoint(self, path: str, step: int) -> None:  # pragma: no cover
-        raise NotImplementedError
+    def restore(self, path: str) -> int:
+        import orbax.checkpoint as ocp
 
-    def restore(self, path: str) -> int:  # pragma: no cover
-        raise NotImplementedError
+        ckptr = ocp.PyTreeCheckpointer()
+        steps = sorted(int(p.name.split("_")[1]) for p in Path(path).glob("step_*"))
+        if not steps:
+            return 0
+        last = steps[-1]
+        self._trainer.state = ckptr.restore(Path(path) / f"step_{last}")
+        return last
 
-    def step(self, batch: Any) -> dict[str, jax.Array]:  # pragma: no cover
-        raise NotImplementedError
+    def step(self, batch: Any) -> dict[str, jax.Array]:
+        # Tunix trainers expose .step(batch) returning a dict-like metrics object.
+        out = self._trainer.step(batch)
+        return dict(out) if not isinstance(out, dict) else out
diff --git a/src/cts/data/apps.py b/src/cts/data/apps.py
index 05c1258..1890834 100644
--- a/src/cts/data/apps.py
+++ b/src/cts/data/apps.py
@@ -51,9 +51,7 @@ def tests_map(split: str = "test", limit: int | None = None) -> dict[str, list[s
         outputs = io.get("outputs", [])
         asserts: list[str] = []
         for x, y in zip(inputs, outputs, strict=False):
-            asserts.append(
-                f"assert str(solve({x!r})).strip() == str({y!r}).strip()"
-            )
+            asserts.append(f"assert str(solve({x!r})).strip() == str({y!r}).strip()")
         if asserts:
             out[p["task_id"]] = asserts
     return out
diff --git a/src/cts/data/schema.py b/src/cts/data/schema.py
index 4dac335..e295b23 100644
--- a/src/cts/data/schema.py
+++ b/src/cts/data/schema.py
@@ -51,8 +51,14 @@ class Quadruple(BaseModel):
 
     def content_hash(self) -> str:
         payload = json.dumps(
-            {"x": self.x, "y0": self.y0, "f": self.f, "y1": self.y1,
-             "task_id": self.meta.task_id, "domain": self.meta.domain},
+            {
+                "x": self.x,
+                "y0": self.y0,
+                "f": self.f,
+                "y1": self.y1,
+                "task_id": self.meta.task_id,
+                "domain": self.meta.domain,
+            },
             sort_keys=True,
         )
         return hashlib.sha256(payload.encode()).hexdigest()[:16]
@@ -61,7 +67,7 @@ def to_jsonl(self) -> str:
         return self.model_dump_json()
 
     @classmethod
-    def from_jsonl(cls, line: str) -> "Quadruple":
+    def from_jsonl(cls, line: str) -> Quadruple:
         return cls.model_validate_json(line)
 
 
diff --git a/src/cts/eval/human_eval.py b/src/cts/eval/human_eval.py
index c62908e..8fa0f7a 100644
--- a/src/cts/eval/human_eval.py
+++ b/src/cts/eval/human_eval.py
@@ -10,8 +10,8 @@
 import csv
 import json
 import random
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Sequence
 
 from ..data.schema import Quadruple
 
@@ -44,7 +44,13 @@ def export_blinded(
                 "candidate_right": right,
             }
         )
-        key.append({"row_id": i, "left": label_b if swap else label_a, "right": label_a if swap else label_b})
+        key.append(
+            {
+                "row_id": i,
+                "left": label_b if swap else label_a,
+                "right": label_a if swap else label_b,
+            }
+        )
     csv_path = out_dir / "human_eval.csv"
     key_path = out_dir / "human_eval.key.json"
     with csv_path.open("w", newline="", encoding="utf-8") as fh:
diff --git a/src/cts/eval/run_eval.py b/src/cts/eval/run_eval.py
index d147cba..8773325 100644
--- a/src/cts/eval/run_eval.py
+++ b/src/cts/eval/run_eval.py
@@ -46,9 +46,7 @@ def evaluate(
         buckets[k]["y0"].append(a)
         buckets[k]["y1"].append(b)
     per_bucket = {
-        str(k): delta_critique(
-            [q for q in quads if bucket_horizon(q) == k], v["y0"], v["y1"]
-        )
+        str(k): delta_critique([q for q in quads if bucket_horizon(q) == k], v["y0"], v["y1"])
         for k, v in buckets.items()
     }
     return {"headline": headline, "bootstrap": boot, "wilcoxon": wx, "buckets": per_bucket}
diff --git a/src/cts/eval/stats.py b/src/cts/eval/stats.py
index 92815d5..09ba8b8 100644
--- a/src/cts/eval/stats.py
+++ b/src/cts/eval/stats.py
@@ -53,7 +53,6 @@ def wilcoxon_signed_rank(a: Sequence[float], b: Sequence[float]) -> dict[str, fl
         i = j + 1
     signed = np.where(d > 0, ranks, -ranks)
     W = float(signed.sum())
-    mean_W = 0.0
     var_W = n * (n + 1) * (2 * n + 1) / 6.0
     z = W / (var_W**0.5) if var_W > 0 else 0.0
     # two-sided p via standard normal
@@ -84,7 +83,6 @@ def cohens_kappa(rater_a: Sequence[int], rater_b: Sequence[int]) -> float:
     if a.size == 0:
         return 0.0
     labels = np.unique(np.concatenate([a, b]))
-    n = a.size
     po = float((a == b).mean())
     pe = 0.0
     for lab in labels:
diff --git a/src/cts/losses/decode_anchor.py b/src/cts/losses/decode_anchor.py
index b274e76..5601cff 100644
--- a/src/cts/losses/decode_anchor.py
+++ b/src/cts/losses/decode_anchor.py
@@ -12,8 +12,8 @@
 
 
 def decode_anchor_loss(
-    logits: jax.Array,       # [B, T, V]  logits for teacher-forced targets
-    target_tokens: jax.Array, # [B, T]     next-token targets
+    logits: jax.Array,  # [B, T, V]  logits for teacher-forced targets
+    target_tokens: jax.Array,  # [B, T]     next-token targets
     mask: jax.Array | None = None,  # [B, T] 1 where we supervise y1 tokens, 0 on prompt
 ) -> jax.Array:
     """Mean token-level NLL over the masked positions (typically y1 tokens)."""
diff --git a/src/cts/losses/energy_loss.py b/src/cts/losses/energy_loss.py
index 3be2608..1524eac 100644
--- a/src/cts/losses/energy_loss.py
+++ b/src/cts/losses/energy_loss.py
@@ -13,7 +13,7 @@
 
 
 def energy_contrastive_loss(
-    e_bad: jax.Array,   # [B] scalar energy assigned to (R_bad, e_f)
+    e_bad: jax.Array,  # [B] scalar energy assigned to (R_bad, e_f)
     e_good: jax.Array,  # [B] scalar energy assigned to (R_good, e_f)
     *,
     margin: float = 1.0,
diff --git a/src/cts/losses/sinkhorn.py b/src/cts/losses/sinkhorn.py
index d170c78..eb9daa0 100644
--- a/src/cts/losses/sinkhorn.py
+++ b/src/cts/losses/sinkhorn.py
@@ -62,8 +62,8 @@ def _step(carry, _):
 
 
 def sinkhorn_divergence_loss(
-    R_a: jax.Array,        # [B, T_a, D]
-    R_b: jax.Array,        # [B, T_b, D]
+    R_a: jax.Array,  # [B, T_a, D]
+    R_b: jax.Array,  # [B, T_b, D]
     *,
     epsilon: float = 0.1,
     time_alpha: float = 0.2,
@@ -93,7 +93,6 @@ def sinkhorn_divergence_loss(
             from ott.tools.sinkhorn_divergence import sinkhorn_divergence
 
             def per_batch(a: jax.Array, b: jax.Array) -> jax.Array:
-                geom = pointcloud.PointCloud(a, b, epsilon=epsilon)
                 kwargs: dict = {"max_iterations": num_iters}
                 if unbalanced_tau is not None:
                     kwargs.update({"tau_a": unbalanced_tau, "tau_b": unbalanced_tau})
@@ -106,6 +105,6 @@ def per_batch(a: jax.Array, b: jax.Array) -> jax.Array:
         except Exception:  # pragma: no cover - fallback path
             pass
 
-    return jax.vmap(
-        lambda a, b: _fallback_sinkhorn(a, b, epsilon=epsilon, num_iters=num_iters)
-    )(R_a_t, R_b_t).mean()
+    return jax.vmap(lambda a, b: _fallback_sinkhorn(a, b, epsilon=epsilon, num_iters=num_iters))(
+        R_a_t, R_b_t
+    ).mean()
diff --git a/src/cts/methods/_batch.py b/src/cts/methods/_batch.py
index c567f5b..efae60e 100644
--- a/src/cts/methods/_batch.py
+++ b/src/cts/methods/_batch.py
@@ -16,12 +16,7 @@
 from ..data.schema import Quadruple
 from ..utils.tokenizer import BOS, EOS, PAD, SEP, ByteTokenizer
 
-_PROMPT_TEMPLATE = (
-    "PROB: {x}\n"
-    "ATT0: {y0}\n"
-    "CRIT: {f}\n"
-    "REV1:"
-)
+_PROMPT_TEMPLATE = "PROB: {x}\nATT0: {y0}\nCRIT: {f}\nREV1:"
 
 
 def prompt_template_hash() -> str:
@@ -34,11 +29,11 @@ def render_prompt(q: Quadruple) -> str:
 
 @dataclass
 class TokenizedBatch:
-    input_ids: jnp.ndarray     # [B, T]  prompt + y1 tokens
-    target_ids: jnp.ndarray    # [B, T]  next-token targets (shift of input_ids)
-    y1_mask: jnp.ndarray       # [B, T]  1 on y1 positions, 0 elsewhere
-    prompt_lens: jnp.ndarray   # [B]     length of prompt region in input_ids
-    total_lens: jnp.ndarray    # [B]
+    input_ids: jnp.ndarray  # [B, T]  prompt + y1 tokens
+    target_ids: jnp.ndarray  # [B, T]  next-token targets (shift of input_ids)
+    y1_mask: jnp.ndarray  # [B, T]  1 on y1 positions, 0 elsewhere
+    prompt_lens: jnp.ndarray  # [B]     length of prompt region in input_ids
+    total_lens: jnp.ndarray  # [B]
 
 
 def encode_batch(
diff --git a/src/cts/methods/b1_dpo.py b/src/cts/methods/b1_dpo.py
index 26e5376..c96a56a 100644
--- a/src/cts/methods/b1_dpo.py
+++ b/src/cts/methods/b1_dpo.py
@@ -9,7 +9,6 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
 
 import jax
 import jax.numpy as jnp
diff --git a/src/cts/methods/b2_grpo_outcome.py b/src/cts/methods/b2_grpo_outcome.py
index cd0009d..7669238 100644
--- a/src/cts/methods/b2_grpo_outcome.py
+++ b/src/cts/methods/b2_grpo_outcome.py
@@ -9,12 +9,11 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
 
 import jax
 import jax.numpy as jnp
 
-from ..backends.local_api import DecodeCfg, LocalModelAPI
+from ..backends.local_api import LocalModelAPI
 from ..rl.grpo import GRPOCfg, approx_kl, group_relative_advantages, grpo_surrogate_loss
 
 NAME = "grpo_outcome"
@@ -22,11 +21,11 @@
 
 @dataclass
 class GRPOBatch:
-    prompt_ids: jnp.ndarray      # [B, T_in]
+    prompt_ids: jnp.ndarray  # [B, T_in]
     completion_ids: jnp.ndarray  # [B, G, T_out]
-    mask: jnp.ndarray            # [B, G, T_out]
-    logprobs_old: jnp.ndarray    # [B, G, T_out]
-    rewards: jnp.ndarray         # [B, G]
+    mask: jnp.ndarray  # [B, G, T_out]
+    logprobs_old: jnp.ndarray  # [B, G, T_out]
+    rewards: jnp.ndarray  # [B, G]
 
 
 def step(
@@ -46,8 +45,8 @@ def step(
     # logits at position i predict token i+1. Completion tokens occupy positions
     # [T_in, T_in+T-1] in `full`; their generation logprobs are therefore at
     # prediction positions [T_in-1, T_in+T-2]. Slice those directly.
-    tgt = full[:, 1:]                               # [B*G, L-1] — next-token targets
-    logp_next = logp[:, :-1, :]                     # [B*G, L-1, V] — aligned predictions
+    tgt = full[:, 1:]  # [B*G, L-1] — next-token targets
+    logp_next = logp[:, :-1, :]  # [B*G, L-1, V] — aligned predictions
     tok_lp = jnp.take_along_axis(logp_next, tgt[..., None], axis=-1)[..., 0]  # [B*G, L-1]
     comp_slice = tok_lp[:, -T:].reshape(B, G, T)
 
diff --git a/src/cts/methods/b5_cts.py b/src/cts/methods/b5_cts.py
index 52b59bc..408bb6f 100644
--- a/src/cts/methods/b5_cts.py
+++ b/src/cts/methods/b5_cts.py
@@ -36,13 +36,13 @@
 
 @dataclass
 class CTSCfg:
-    lam_align: float = 1.0   # λ₁
+    lam_align: float = 1.0  # λ₁
     lam_decode: float = 1.0  # λ₂
     lam_energy: float = 0.1  # λ₃
     edit_eta: float = 0.1
     # Ablations
-    use_ref_trajectory: bool = True     # if False, align only to R*
-    online_critique: bool = False       # A6: refresh critique from current policy each step
+    use_ref_trajectory: bool = True  # if False, align only to R*
+    online_critique: bool = False  # A6: refresh critique from current policy each step
     # Sub-configs
     alignment: AlignmentCfg = field(default_factory=AlignmentCfg)
     projection: ProjectionCfg = field(default_factory=ProjectionCfg)
@@ -80,14 +80,14 @@ class CTSBatch:
     """
 
     grpo: GRPOBatch
-    input_ids: jnp.ndarray                 # [B, T] prompt + y1 (decode anchor)
-    target_ids: jnp.ndarray                # [B, T]
-    y1_mask: jnp.ndarray                   # [B, T]
-    critique_ids: jnp.ndarray              # [B, T_f]
+    input_ids: jnp.ndarray  # [B, T] prompt + y1 (decode anchor)
+    target_ids: jnp.ndarray  # [B, T]
+    y1_mask: jnp.ndarray  # [B, T]
+    critique_ids: jnp.ndarray  # [B, T_f]
     ref_hidden: jnp.ndarray | None = None  # [B, T_ref, D] teacher-forced y1 hidden
     bad_hidden: jnp.ndarray | None = None  # [B, T_bad, D] teacher-forced y0 hidden
-    bad_input_ids: jnp.ndarray | None = None   # [B, T_bad] y0 path token ids
-    ref_input_ids: jnp.ndarray | None = None   # [B, T_ref] y1 path token ids
+    bad_input_ids: jnp.ndarray | None = None  # [B, T_bad] y0 path token ids
+    ref_input_ids: jnp.ndarray | None = None  # [B, T_ref] y1 path token ids
 
 
 def _critic_view(critic: EnergyCritic, *, freeze: bool) -> EnergyCritic:
@@ -125,8 +125,8 @@ def step(
     out_grpo = model.forward(full)
     logp = jax.nn.log_softmax(out_grpo.logits, axis=-1)
     # Align logits→next-token correctly: logit[i] predicts token i+1.
-    tgt = full[:, 1:]                       # [B*G, L-1]
-    logp_next = logp[:, :-1, :]             # [B*G, L-1, V]
+    tgt = full[:, 1:]  # [B*G, L-1]
+    logp_next = logp[:, :-1, :]  # [B*G, L-1, V]
     tok_lp = jnp.take_along_axis(logp_next, tgt[..., None], axis=-1)[..., 0]
     comp_slice = tok_lp[:, -T:].reshape(B, G, T)
     A = group_relative_advantages(g.rewards, cfg.grpo.eps)
@@ -150,7 +150,7 @@ def step(
     else:
         assert batch.bad_hidden is not None, "CTSBatch needs bad_input_ids or bad_hidden"
         bad_hidden = batch.bad_hidden
-    R_theta = modules.projection(bad_hidden)          # [B, T_bad, r_dim]
+    R_theta = modules.projection(bad_hidden)  # [B, T_bad, r_dim]
 
     # Build R*. Freeze the critic parameters when A5 ablation is on — we still
     # need gradient w.r.t. R (inputs) for the edit step, so we sg the critic's
@@ -175,8 +175,11 @@ def step(
     # The energy term is about training E_psi, so detach hidden → no backbone
     # grad here; and when A5 freeze is set, also detach the critic's params so
     # this loss is a no-op on psi.
-    R_good_in = jax.lax.stop_gradient(ref_hidden) if cfg.use_ref_trajectory and batch.ref_input_ids is not None \
-                else (jax.lax.stop_gradient(batch.ref_hidden) if batch.ref_hidden is not None else None)
+    R_good_in = (
+        jax.lax.stop_gradient(ref_hidden)
+        if cfg.use_ref_trajectory and batch.ref_input_ids is not None
+        else (jax.lax.stop_gradient(batch.ref_hidden) if batch.ref_hidden is not None else None)
+    )
     if R_good_in is None:
         # No ref available (cfg.use_ref_trajectory=False and no raw ref_hidden) — skip energy loss.
         e_loss = jnp.asarray(0.0)
diff --git a/src/cts/rl/grpo.py b/src/cts/rl/grpo.py
index 8232e4e..beacdb9 100644
--- a/src/cts/rl/grpo.py
+++ b/src/cts/rl/grpo.py
@@ -35,8 +35,8 @@ def group_relative_advantages(rewards: jax.Array, eps: float = 1e-6) -> jax.Arra
 def grpo_surrogate_loss(
     logprobs_new: jax.Array,  # [B, G, T]
     logprobs_old: jax.Array,  # [B, G, T] (stop-grad)
-    mask: jax.Array,          # [B, G, T]
-    advantages: jax.Array,    # [B, G]
+    mask: jax.Array,  # [B, G, T]
+    advantages: jax.Array,  # [B, G]
     cfg: GRPOCfg,
 ) -> jax.Array:
     logprobs_old = jax.lax.stop_gradient(logprobs_old)
diff --git a/src/cts/rl/rollout.py b/src/cts/rl/rollout.py
index 27af444..8255846 100644
--- a/src/cts/rl/rollout.py
+++ b/src/cts/rl/rollout.py
@@ -16,8 +16,8 @@
 
 @dataclass
 class Rollout:
-    tokens: jax.Array    # [B, T_out]
-    hidden: jax.Array    # [B, T_out, D]
+    tokens: jax.Array  # [B, T_out]
+    hidden: jax.Array  # [B, T_out, D]
     logprobs: jax.Array  # [B, T_out]
 
 
diff --git a/src/cts/rl/verifier.py b/src/cts/rl/verifier.py
index ae831b2..6b6c95e 100644
--- a/src/cts/rl/verifier.py
+++ b/src/cts/rl/verifier.py
@@ -7,6 +7,7 @@
 
 from __future__ import annotations
 
+import contextlib
 import math
 import re
 import resource
@@ -71,10 +72,8 @@ def _run(self, code: str) -> bool:
         def limit_resources() -> None:  # pragma: no cover — subprocess init
             resource.setrlimit(resource.RLIMIT_CPU, (int(self.timeout_sec) + 1,) * 2)
             bytes_ = self.memory_mb * 1024 * 1024
-            try:
+            with contextlib.suppress(Exception):
                 resource.setrlimit(resource.RLIMIT_AS, (bytes_, bytes_))
-            except Exception:
-                pass
 
         with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as fh:
             fh.write(code)
@@ -91,10 +90,8 @@ def limit_resources() -> None:  # pragma: no cover — subprocess init
         except subprocess.TimeoutExpired:
             return False
         finally:
-            try:
+            with contextlib.suppress(Exception):
                 path.unlink(missing_ok=True)
-            except Exception:
-                pass
 
     def score(self, task_id: str, output: str) -> float:
         tests = self.tests.get(task_id)
diff --git a/src/cts/train/loop.py b/src/cts/train/loop.py
index 0e4463e..3b656b2 100644
--- a/src/cts/train/loop.py
+++ b/src/cts/train/loop.py
@@ -7,9 +7,10 @@
 
 from __future__ import annotations
 
+from collections.abc import Callable
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Callable
+from typing import Any
 
 import jax
 import optax
@@ -74,7 +75,7 @@ def train_local(
     rests = tuple(x[2] for x in graphdefs_and_rest)
 
     def _merge(params):
-        return tuple(nnx.merge(g, p, r) for g, p, r in zip(graphdefs, params, rests))
+        return tuple(nnx.merge(g, p, r) for g, p, r in zip(graphdefs, params, rests, strict=False))
 
     base_opt = _make_optimizer(cfg)
     if freeze_mask is not None:
@@ -90,7 +91,8 @@ def _merge(params):
     logger = JSONLLogger(Path(log_dir) / "train.jsonl")
 
     for step in range(cfg.max_steps):
-        def loss_and_metrics(params):
+
+        def loss_and_metrics(params, step=step):
             merged = _merge(params)
             if multi:
                 return step_fn(*merged, step)
@@ -104,7 +106,7 @@ def loss_and_metrics(params):
             logger.log(step, {"loss": float(loss), **{k: float(v) for k, v in metrics.items()}})
 
     # Write final params back to the user-visible module objects (outside trace).
-    for m, p in zip(modules, params):
+    for m, p in zip(modules, params, strict=False):
         nnx.update(m, p)
 
     logger.close()
diff --git a/src/cts/train/main.py b/src/cts/train/main.py
index e5e10bd..44901a8 100644
--- a/src/cts/train/main.py
+++ b/src/cts/train/main.py
@@ -50,6 +50,11 @@ def _load_cfg(overrides: list[str]):
 def main() -> None:
     ap = argparse.ArgumentParser()
     ap.add_argument("overrides", nargs="*")
+    ap.add_argument(
+        "--gcs-bucket",
+        default=None,
+        help="If set (gs://…), rsync artifacts/ to this prefix at end of run.",
+    )
     args = ap.parse_args()
 
     cfg = _load_cfg(args.overrides)
@@ -58,10 +63,17 @@ def main() -> None:
     print(f"[cts] fairness fingerprint: {fp}")
 
     # Backend
-    if cfg.backend.kind != "local_nano":
+    if cfg.backend.kind == "tunix":
+        from ..backends.tunix_adapter import TunixAdapter
+
+        adapter = TunixAdapter(cfg)
+        print(f"[cts] tunix trainer ready: {type(adapter.trainer_handle()).__name__}")
+        # Real training delegated to the Tunix trainer; one step() so smoke
+        # surfaces wiring problems immediately.
+        # (The replay shard load + batch encode below is shared with local.)
+    elif cfg.backend.kind != "local_nano":
         raise NotImplementedError(
-            "Main entry currently wires only local_nano; tunix/maxtext land in M8/M9 "
-            "(see cts.backends.tunix_adapter / maxtext_adapter)."
+            f"Backend {cfg.backend.kind!r} not wired yet; see cts.backends.{cfg.backend.kind}_adapter."
         )
     nano_cfg = NanoLMConfig(
         vocab_size=cfg.backend.nano.vocab_size,
@@ -99,6 +111,16 @@ def main() -> None:
     print(f"[cts] step0 loss={float(loss):.4f}")
     print(f"[cts] metrics={ {k: float(v) for k, v in metrics.items()} }")
 
+    if args.gcs_bucket:
+        # Defer import so non-GCP runs don't pay for the dep.
+        import sys as _sys
+
+        _deploy = Path(__file__).resolve().parents[3] / "deploy"
+        _sys.path.insert(0, str(_deploy))
+        from gcs_sync import upload  # type: ignore[import-not-found]
+
+        upload("artifacts/", args.gcs_bucket.rstrip("/") + f"/{cfg.out_dir}")
+
 
 if __name__ == "__main__":
     main()
diff --git a/tests/integration/test_main_entry.py b/tests/integration/test_main_entry.py
index b09e493..fc1c244 100644
--- a/tests/integration/test_main_entry.py
+++ b/tests/integration/test_main_entry.py
@@ -11,10 +11,19 @@
 @pytest.mark.parametrize("method", ["sft", "dpo"])
 def test_main_entry_runs(method: str):
     proc = subprocess.run(
-        [sys.executable, "-m", "cts.train.main",
-         f"method={method}", "task=fixtures_math", "backend=local_nano",
-         "trainer.max_steps=1", "trainer.batch_size=2"],
-        capture_output=True, text=True, timeout=120,
+        [
+            sys.executable,
+            "-m",
+            "cts.train.main",
+            f"method={method}",
+            "task=fixtures_math",
+            "backend=local_nano",
+            "trainer.max_steps=1",
+            "trainer.batch_size=2",
+        ],
+        capture_output=True,
+        text=True,
+        timeout=120,
     )
     assert proc.returncode == 0, proc.stderr
     assert "loss=" in proc.stdout or "step0" in proc.stdout
diff --git a/tests/integration/test_methods_smoke.py b/tests/integration/test_methods_smoke.py
index 5e7eea5..4b26111 100644
--- a/tests/integration/test_methods_smoke.py
+++ b/tests/integration/test_methods_smoke.py
@@ -56,7 +56,9 @@ def _mk_grpo_batch(model: NanoLM, B: int = 2, G: int = 2, T_in: int = 8, T_out:
     mask = jnp.ones((B, G, T_out))
     old_lp = jax.random.normal(jax.random.PRNGKey(2), (B, G, T_out)) * 0.01
     rewards = jax.random.uniform(jax.random.PRNGKey(3), (B, G))
-    return GRPOBatch(prompt_ids=prompt, completion_ids=comp, mask=mask, logprobs_old=old_lp, rewards=rewards)
+    return GRPOBatch(
+        prompt_ids=prompt, completion_ids=comp, mask=mask, logprobs_old=old_lp, rewards=rewards
+    )
 
 
 def test_b2_grpo_outcome(model_and_tok):
diff --git a/tests/unit/test_bug_fixes.py b/tests/unit/test_bug_fixes.py
index 570256c..9fabd32 100644
--- a/tests/unit/test_bug_fixes.py
+++ b/tests/unit/test_bug_fixes.py
@@ -31,11 +31,14 @@ def _grpo_batch(model, B=2, G=2, T_in=5, T_out=4):
     mask = jnp.ones((B, G, T_out))
     old_lp = jnp.zeros((B, G, T_out))
     rewards = jax.random.uniform(jax.random.PRNGKey(3), (B, G))
-    return GRPOBatch(prompt_ids=prompt, completion_ids=comp, mask=mask, logprobs_old=old_lp, rewards=rewards)
+    return GRPOBatch(
+        prompt_ids=prompt, completion_ids=comp, mask=mask, logprobs_old=old_lp, rewards=rewards
+    )
 
 
 # ---- Bug #1: GRPO next-token alignment ---------------------------------------
 
+
 def test_grpo_token_logprobs_match_manual_alignment():
     """The internal ``comp_slice`` must equal log π_θ(c_t | prompt + c_{<t})
     computed by hand — i.e. logit at position (prompt_len + t - 1) on token c_t."""
@@ -53,9 +56,7 @@ def test_grpo_token_logprobs_match_manual_alignment():
     # prediction position T_in + t - 1, for t in [0, T-1].
     expected = jnp.stack(
         [
-            jnp.take_along_axis(
-                logp[:, T_in + t - 1, :], full[:, T_in + t][:, None], axis=-1
-            )[:, 0]
+            jnp.take_along_axis(logp[:, T_in + t - 1, :], full[:, T_in + t][:, None], axis=-1)[:, 0]
             for t in range(T)
         ],
         axis=-1,
@@ -83,6 +84,7 @@ def test_grpo_step_has_no_padding_position():
 
 # ---- Bug #2: backbone gradient through alignment -----------------------------
 
+
 def _cts_setup(model, use_input_ids: bool, B=2, T=10):
     cfg = CTSCfg(lam_align=1.0, lam_decode=0.0, lam_energy=0.0)
     cfg.projection.hidden_size = model.hidden_size
@@ -93,7 +95,10 @@ def _cts_setup(model, use_input_ids: bool, B=2, T=10):
     modules = CTSModules(cfg, rngs=nnx.Rngs(0))
 
     grpo = _grpo_batch(model)
-    ids = lambda k: jax.random.randint(jax.random.PRNGKey(k), (B, T), 0, model.vocab_size)
+
+    def ids(k):
+        return jax.random.randint(jax.random.PRNGKey(k), (B, T), 0, model.vocab_size)
+
     if use_input_ids:
         batch = CTSBatch(
             grpo=grpo,
@@ -168,6 +173,7 @@ def test_alignment_reaches_backbone_via_bad_input_ids():
 
 # ---- Bug #3: combined param tree in trainer ----------------------------------
 
+
 def test_train_local_updates_cts_modules(tmp_path):
     from cts.train.loop import LoopCfg, train_local
 
@@ -195,7 +201,7 @@ def step_fn(model_, modules_, step):
     def _changed(a, b):
         return sum(
             float(jnp.sum(jnp.abs(x - y)))
-            for x, y in zip(jax.tree.leaves(a), jax.tree.leaves(b))
+            for x, y in zip(jax.tree.leaves(a), jax.tree.leaves(b), strict=False)
         )
 
     assert _changed(before_proj, after_proj) > 0.0
@@ -205,6 +211,7 @@ def _changed(a, b):
 
 # ---- Bug #4: A5 freeze suppresses gradient to E_psi --------------------------
 
+
 def test_a5_freeze_zeros_energy_critic_grad():
     """With cfg.energy.freeze=True, ∂L/∂ψ must be exactly zero, while gradient
     through R (inputs to the critic) is preserved so R* still moves."""
diff --git a/tests/unit/test_losses.py b/tests/unit/test_losses.py
index d374a4e..d09896b 100644
--- a/tests/unit/test_losses.py
+++ b/tests/unit/test_losses.py
@@ -48,7 +48,9 @@ def test_alignment_dispatcher_routes():
     b = jax.random.normal(rng, (2, 5, 4))
     l2 = alignment_loss(a, b, AlignmentCfg(kind="l2", weight=1.0))
     dtw = alignment_loss(a, b, AlignmentCfg(kind="soft_dtw", weight=1.0, gamma=1.0))
-    sk = alignment_loss(a, b, AlignmentCfg(kind="sinkhorn", weight=1.0, num_iters=20, use_ott=False))
+    sk = alignment_loss(
+        a, b, AlignmentCfg(kind="sinkhorn", weight=1.0, num_iters=20, use_ott=False)
+    )
     zero = alignment_loss(a, b, AlignmentCfg(kind="sinkhorn", weight=0.0))
     for v in (l2, dtw, sk):
         assert jnp.isfinite(v)
diff --git a/tests/unit/test_verifier.py b/tests/unit/test_verifier.py
index 61179a6..d4072ab 100644
--- a/tests/unit/test_verifier.py
+++ b/tests/unit/test_verifier.py
@@ -15,9 +15,11 @@ def test_math_verifier():
 
 
 def test_code_verifier_pass_and_fail():
-    v = CodeVerifier(tests={
-        "ok": ["assert add(1,2) == 3"],
-        "bad": ["assert add(1,2) == 3"],
-    })
+    v = CodeVerifier(
+        tests={
+            "ok": ["assert add(1,2) == 3"],
+            "bad": ["assert add(1,2) == 3"],
+        }
+    )
     assert v.score("ok", "def add(a,b):\n    return a+b") == 1.0
     assert v.score("bad", "def add(a,b):\n    return a-b") == 0.0
diff --git a/uv.lock b/uv.lock
index 4d117b8..a699fb4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -261,6 +261,88 @@ wheels = [
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 ]
 
+[[package]]
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+version = "2.0.0"
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+
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 version = "0.0.1"
@@ -469,6 +611,10 @@ dev = [
     { name = "pytest-xdist" },
     { name = "ruff" },
 ]
+gcp = [
+    { name = "google-cloud-aiplatform" },
+    { name = "google-cloud-storage" },
+]
 hf = [
     { name = "datasets" },
     { name = "tokenizers" },
@@ -492,6 +638,8 @@ requires-dist = [
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     { name = "einops", specifier = ">=0.8.0" },
     { name = "flax", specifier = ">=0.10.0" },
+    { name = "google-cloud-aiplatform", marker = "extra == 'gcp'", specifier = ">=1.60" },
+    { name = "google-cloud-storage", marker = "extra == 'gcp'", specifier = ">=2.16" },
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     { name = "jax", specifier = ">=0.4.30" },
     { name = "jaxlib", specifier = ">=0.4.30" },
@@ -511,7 +659,7 @@ requires-dist = [
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     { name = "tunix", marker = "extra == 'scale'" },
 ]
-provides-extras = ["ot", "hf", "teacher", "scale", "dev"]
+provides-extras = ["ot", "hf", "teacher", "scale", "gcp", "dev"]
 
 [[package]]
 name = "dataclasses-json"
@@ -886,6 +1034,308 @@ http = [
     { name = "aiohttp" },
 ]
 
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+    { name = "googleapis-common-protos" },
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