CooperBench/qwen35-9b-reasoning-share-coop-random-50
What this is Cooperative two-agent coding dataset: 50 task pairs across 15 repos (random-50 subset), generated with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a reasoning-share prompt variant — agents share their internal reasoning and analysis with each other before and during implementation, giving each agent visibility into the other's thought process to improve integration. All 50 pairs were successfully evaluated. At a glance Field Value… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/qwen35-9b-reasoning-share-coop-random-50.
What this is
Cooperative two-agent coding dataset: 50 task pairs across 15 repos (random-50 subset), generated with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a reasoning-share prompt variant — agents share their internal reasoning and analysis with each other before and during implementation, giving each agent visibility into the other's thought process to improve integration. All 50 pairs were successfully evaluated.
At a glance
How it was generated
cooperbench run --setting coop -a mini_swe_agent -c 30 qwen35-9b-reasoning-share-coop-random-50Model served via vLLM OpenAI-compatible endpoint (openai/Qwen/Qwen3.5-9B). Reasoning-share variant: agents explicitly share their reasoning and analysis steps with each other to give insight into how each is approaching their feature.
File layout
index.csv— one row per task pair; HF Dataset Viewer entry pointqwen35-9b-reasoning-share-coop-random-50/coop/<repo>/<task_id>/<features>/— raw per-pair artifacts:result.json,eval.json,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.
metadata JSON carries per-agent statuses, steps, merge outcome, per-feature pass — 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 (~27.8M total in+out).
Caveats
- random-50 subset — all 50 tasks completed
- 11.0% LimitsExceeded exits (11/100 agent slots); 3.0% Error (3/100)
- 38.0% merge conflict rate (19/50)
- Token fields in
result.jsonare 0; aggregate fromagent_full_traj.jsonif needed
Citation
@dataset{qwen35_9b_reasoning_share_coop_random_50,
title = {qwen35-9b-reasoning-share-coop-random-50},
author = {Arya Prabhudesai},
year = {2026},
url = {https://huggingface.co/datasets/CooperBench/qwen35-9b-reasoning-share-coop-random-50}
}