| --- |
| license: mit |
| tags: |
| - swe-bench |
| - reinforcement-learning |
| - agentic |
| - rollouts |
| --- |
| |
| # combo2 RL rollouts — Qwen3.5-35B-A3B |
|
|
| Complete rollout + reward record for the **combo2** GRPO run: every trajectory the policy |
| generated during training, with its graded reward. Preserved so the run stays re-analysable |
| after its torch_dist checkpoints were retired. |
| |
| ## Run |
| |
| | | | |
| |---|---| |
| | base model | Qwen3.5-35B-A3B | |
| | init | `sweagent/practical-diffrecon-ep2` (the iter-1 RFT ckpt) | |
| | harness | combo (contract-ground + git-add-N + wall-clock valve) | |
| | algorithm | GRPO with dynamic sampling, `kl-loss-coef 0.00`, lr 1e-6 constant | |
| | batch | rollout-batch 32 x n-samples 4, GBS 128 | |
| | steps | 0-54 | |
| | task pool | `combined_0630` (SWE-rebench V1/V2 + Scale-SWE) | |
| | grading | azure-modal sandbox, F2P/P2P; `resolved` = full pass | |
|
|
| **Superseded by** the base -> iter_49 -> iter-2 line; published as a record, not as the current method. |
| |
| ## Contents |
| |
| | file | items | what | |
| |---|---|---| |
| | `trajectories_00..05.tar.gz` | 27,787 | full trajectories: messages, `model_patch`, `exit_status`, `n_steps`, prompt/response token_ids, loss_mask | |
| | `rewards.tar.gz` | 26,610 | per-sample grading: `resolved`, `resolution`, `tests_run/passed`, f2p/p2p rates, `error` | |
| | `group_info.tar.gz` | 2,038 | per-instance group records (`saved_at`, `reward`, `sample_index`, `token_length`) | |
|
|
| 34.8 GiB raw, ~2.8 GB gzipped. Files are named `<instance_id>_<sample_index>.json` |
| (trajectory) and `<instance_id>_<sample_index>_rewards.json` (reward), so the two join on |
| name. |
|
|
| ## Notes for re-analysis |
|
|
| - **Not every trajectory has a reward.** Rollouts with no patch (`exit_status` of |
| `LimitsExceeded` / `TimeExceeded`) are never graded — they count as unresolved. The |
| ~1,200 gap between traj and reward counts is that, not data loss. |
| - **Per-step binning**: these files carry no step field. Bin by file mtime against the |
| training log's `rollout N:` markers, assigning a sample to the **first marker at/after** |
| its mtime — that line prints when a finished batch is handed to `actor_train`, so a |
| step's rollouts precede its marker. |
| - **`resolved` is the ground truth**, not the in-pipeline `resolve=N/N` stdout print, which |
| is known to read 0. |
|
|
