---
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**Superseded by** the base -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.