base_model, tags, library_name
| base_model | tags | library_name | ||||
|---|---|---|---|---|---|---|
| laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink |
|
transformers |
ablation-pymethods2test-seqmean-arm0-30-8B
RL (SkyRL GRPO) checkpoint from the sequence-mean / RLOO-n (arm0) ablation of
the a3-successor study. The policy loss uses loss_reduction=sequence_mean with
the advantage_estimator=rloo_n (RLOO-n) estimator, contrasting with the
token-mean reduction of the a3 series.
- Base model: laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink (a Qwen3-8B SFT)
- Training dataset: DCAgent/exp_rpt_pymethods2test-large
- Checkpoint:
global_step_30. - Training: SkyRL GRPO,
hf_save_interval=5, 14x GH200 nodes on JSC Jupiter.
The rl_config.yaml in this repo is the exact launch config used for reproducibility.
This is the step-30 checkpoint of the same run that produced
laion/ablation-pymethods2test-seqmean-arm0-15-8B (step 15).
Training Traces
Training-time Daytona/Harbor rollouts for this run are uploaded as a companion dataset: penfever/ablation-pymethods2test-seqmean-arm0
The dataset contains the last episode of each trial (per
make_and_upload_trace_dataset --episodes last) — the same rollouts
the policy was trained on after rollback / truncation.