Cowork-Gym: Synthesizing Interactive Work Environments at Scale
We introduce Cowork-Gym, a framework that synthesizes Cowork environments through a build-execute-revise loop that uses execution evidence and review feedback to validate and repair generated environments.
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Key points
- Cowork agents navigate dependencies across tools and files while adapting to changing user requirements and edits to shared workspaces.
- Scaling such environments requires maintaining consistency among task objectives, executable resources, user behavior, and success criteria.
- To capture realistic Cowork dynamics, a stateful user simulator responds to the agent's actions through dialogue and direct edits to the shared workspace.
- We synthesize 64.9K environments across 14 application scenarios and use them to post-train 9B, 27B, and 35B-A3B Qwen models with supervised fine-tuning, reinforcement learning, and on-policy distillation.
Sources (2)
- [1]Cowork-Gym: Synthesizing Interactive Work Environments at ScalearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 03:56 AM
We introduce Cowork-Gym, a framework that synthesizes Cowork environments through a build-execute-revise loop that uses execution evidence and review feedback to validate and repair generated environments.
Cowork agents navigate dependencies across tools and files while adapting to changing user requirements and edits to shared workspaces.
- [2]Cowork-Gym: Synthesizing Interactive Work Environments at ScaleHugging Face Daily Papers · Oct 13, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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