code/GPU_HANDOFF.md

GPU handoff: Stage-1 SFT

Required machine

• One RTX A6000 (48 GB) or equivalent NVIDIA GPU
• Linux with a working CUDA/PyTorch stack
• At least 80 GB free disk
• Access to the selected base checkpoint

The CPU artifacts are ready. Do not regenerate or edit the frozen evaluation manifest on the GPU machine.

Immutable inputs

• Dataset: data/arena_sft_stage1
• Dataset version: arena-sft-v2
• Dataset content SHA-256: edad09bb301748621a0fab73ebf3de60d60abfd9f56c9afcc6ca02ffe12f3a80
• Prompt version: arena-v2-structured-priority
• Environment version: arena-core-v1
• Frozen evaluation manifest SHA-256: b53bfc523043ec71cc69f851d0819511c5a9f0b4f09520898f30954bbe874b29

Re-run before training:

uv run --project experiments/swarm_arena --with pytest \
  pytest experiments/swarm_arena/tests -q
uv run --with ./experiments/swarm_arena \
  python -m swarm_ctf_eval.arena_data_audit \
  data/arena_sft_stage1 --require-split-action-coverage
uv run --with ./experiments/swarm_arena \
  python -m swarm_ctf_eval.arena_eval \
  --provider oracle --output-dir results/arena_v2/oracle

Experiment order

1. Serve the untouched base model and run the frozen arena evaluation. Save every raw response and the summary. This is the pre-SFT baseline.
2. Run the 256-row overfit/smoke experiment. Stop if strict JSON does not approach 100%; that indicates a template or loss-mask problem, not insufficient data.
3. Train LoRA on train.jsonl; use validation.jsonl for checkpoint selection.
4. Select by validation task metrics, not training loss alone.
5. Evaluate the selected checkpoint exactly once on test.jsonl and the frozen 60-case arena manifest.
6. Compare base versus SFT under generated, dropped, reference, and shuffled communication. Do not begin MARL until protocol and mechanics gates pass.

Initial LoRA recipe

• Base: Qwen/Qwen3-4B-Instruct-2507
• Training stack: the pinned Prime-RL branch and its default numerical dtypes
• LoRA rank: 32
• LoRA alpha: 64
• LoRA dropout: 0.05
• Targets: attention Q/K/V/O and MLP gate/up/down projections
• Sequence length: 2048
• Optimizer: AdamW, learning rate 1e-4, weight decay 0.01
• Scheduler: cosine, 3% warmup
• Effective batch size: 32
• Epochs: start with 2; do not extend automatically
• Gradient clipping: 1.0
• Gradient checkpointing: enabled
• Loss: assistant tokens only; system and user tokens masked
• Save/evaluate frequently enough to obtain at least 8 validation measurements

Raw data are broadcast-heavy (4,608 broadcast versus 900 action examples). The published training splits sample 60% broadcast, 31% common actions, and 9% rare WAIT/SCAN/TRANSFER actions. Validation and test remain unweighted.

SFT promotion gates

The selected checkpoint must satisfy all of the following without relaxing the strict parser:

• at least 99.5% exact action-schema validity;
• at least 99% exact broadcast-schema validity;
• zero unsupported broadcast facts on validation;
• at least 95% legal actions;
• improvement over the base model in mean oracle regret;
• no material degradation under action-order permutations;
• no improvement claim if generated messages fail to beat dropped messages or fail to degrade under shuffled-message intervention.

Failure to meet a gate triggers data/prompt diagnosis. It does not justify opening the test set repeatedly or weakening the scorer.