README.md

metadata

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Superseded by the base -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.