CooperBench/qwen35-9b-milestone-checkins-coop

What this is

Cooperative two-agent coding dataset: 211 task pairs across 18 repos, generated with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a milestone-checkins prompt variant — agents use periodic structured check-ins at predefined milestones to coordinate progress and surface integration conflicts early. Patches are auto-merged after both submit.

Coverage caveat: Only 146 of 211 pairs were successfully evaluated (65 had eval errors). The high agent Error rate (29.9%) is this run's primary data quality concern.

At a glance

Field	Value
Model	Qwen/Qwen3.5-9B
Agent	mini_swe_agent (milestone-checkins prompt)
Setting	coop
Repos	18
Pairs (results)	211
Pairs evaluated	146 (65 had eval errors)
Both-pass	2.1% (3/146)
Per-feature pass	16.1% (47/292)
Merge clean rate	65.1% (95/146)
Agent Error rate	29.9% (126/422 slots)
Total tokens	~89.1M (in+out, from traj files)
Owner	Arya Prabhudesai

How it was generated

cooperbench run --setting coop -a mini_swe_agent -c 30 qwen35-9b-milestone-checkins-coop

Model served via vLLM OpenAI-compatible endpoint (openai/Qwen/Qwen3.5-9B). Milestone-checkins variant: agents perform structured coordination check-ins at key milestones during their runs.

File layout

• index.csv — one row per task pair; HF Dataset Viewer entry point
• qwen35-9b-milestone-checkins-coop/coop/<repo>/<task_id>/<features>/ — raw per-pair artifacts: result.json, eval.json (where present), agent1_traj.json, agent2_traj.json, agent{1,2}.patch, conversation.json

log_dir column in index.csv points to the per-pair subdirectory.

Schema highlights for mid-training

Filter on: both_passed=true, model, agent_framework. both_passed=null means eval was not run for that pair (eval error).

metadata JSON carries per-agent statuses, steps, merge outcome, per-feature pass, and eval_missing flag — use json.loads(row["metadata"]) without following the pointer.

Note: total_tokens is 0 for this run — token counts are in agent_full_traj.json under messages[*].extra.response.usage (~89.1M total in+out).

Caveats

• Very high agent Error exit rate: 29.9% (126/422 slots) — likely caused by the milestone-checkins prompt variant triggering unexpected agent behaviour
• 65 pairs have no eval.json (eval errors): flask×12, jinja×9, typeguard×7, oauthlib×6, starlette×6, flake8×5, axios×4, sqlparse×4, trio×3, tweepy×3, pyparsing×3, indicatif×3
• Only 146/211 pairs evaluated — grade/pass-rate stats are partial
• 34.9% merge conflict rate (51/146 evaluated pairs)
• Token fields in result.json are 0; aggregate from agent_full_traj.json if needed

Citation

@dataset{qwen35_9b_milestone_checkins_coop,
  title  = {qwen35-9b-milestone-checkins-coop},
  author = {Arya Prabhudesai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/CooperBench/qwen35-9b-milestone-checkins-coop}
}