| # 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: |
|
|
| ```bash |
| 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. |
|
|
