sweagent/iter2-rl-rollouts
iter-2 RL rollouts (combo_fb, Qwen3.5-35B-A3B) Complete rollout + reward record for the iter-2 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 (only latest-3 survive; the 11 HF milestones at iter_0/4/9/.../44/49 are the durable checkpoint record). Run base model Qwen3.5-35B-A3B init iter_49 of the iter-1 RL run… See the full description on the dataset page: https://huggingface.co/datasets/sweagent/iter2-rl-rollouts.
iter-2 RL rollouts (combo_fb, Qwen3.5-35B-A3B)
Complete rollout + reward record for the iter-2 GRPO run: every trajectory the policy generated during training, with its graded reward. Preserved so the run stays re-analysable after its torchdist checkpoints were retired (only latest-3 survive; the 11 HF milestones at `iter0/4/9/.../44/49` are the durable checkpoint record).
Run
Training was paused at ckpt 49, not run to completion — it stopped for checkpoint evaluation, and --load == --save means it can resume losslessly from iter_49.
Contents
Training and eval rollouts are in SEPARATE archives. The training set is the input to iter-3's hard-task search, so it is usable without filtering.
3.13 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.
Training-set census
Notes for re-analysis
- Training and eval were mixed on disk. Both land in the same trajectory directory, so a naive count gives 22,774 rollouts over 2,710 unique tasks — more tasks than the 2,484-task pool contains, which is the tell. The split here is by membership in the three id lists (
combined_0630_clean,swebench_200_random,swe_multi_100); it accounts for every file with none unclassified. Eval resolves at ~70% against training's 50.7%, so pooling them biases both. - Not every trajectory has a reward. 23,139 trajectories vs 20,248 rewards — a gap of 2,891. Rollouts that produce no patch (
LimitsExceeded/ timeout) are never sent to the sandbox, so they have no reward file. They count as unresolved. This is not data loss. - `never-solved` is not `never-attempted`. 67 of the 2,484 tasks were never sampled at all. A task with 0 rollouts carries no difficulty signal; a task with 8 rollouts and 0 resolves does. Keep them separate when building a hard-task set.
- Two source directories. The run restarted once after an OOM, so rollouts are split across
20260814_060031_*and20260815_091704_*. Any total must sum both — reading only the newer one silently drops roughly the first day. - 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. - No `group_info` in this run (the sibling
combo2-rl-rolloutshas 2,038 records).
Held-out result
SWE-V 500, combofb harness, avg@3, temp 0.95 / topp 0.95 / top_k 20:
Related
sweagent/combo2-rl-rollouts— the equivalent record for the previous run (note: that one is unfiltered, mixing training and eval rollouts together)sweagent/coevolve-swev-grid-trajs— held-out SWE-V evaluation grid