ClawGym: A Scalable Framework for Building Effective Claw Agents

ClawGym: A Scalable Framework for Building Effective Claw Agents

From Daily Paper Cast by Jingwen Liang, Gengyu Wang

May 1, 2026 · 26 min · Episode 1821

About this episode

This episode discusses ClawGym, a framework for developing effective Claw-style agents and synthesizing training data.

🤗 Upvotes: 38 | cs.CL, cs.AI, cs.LG Authors: Fei Bai, Huatong Song, Shuang Sun, Daixuan Cheng, Yike Yang, Chuan Hao, Renyuan Li, Feng Chang, Yuan Wei, Ran Tao, Bryan Dai, Jian Yang, Wayne Xin Zhao Title: ClawGym: A Scalable Framework for Building Effective Claw Agents Arxiv: http://arxiv.org/abs/2604.26904v1 Abstract: Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around these environments remains constrained by the absence of a systematic framework, especially one for synthesizing verifiable training data and integrating it with agent training and diagnostic evaluation. To address this challenge, we present ClawGym, a scalable framework that supports the full lifecycle of Claw-style personal agent development. Concretely, we construct ClawGym-SynData, a diverse dataset of 13.5K filtered tasks synthesized from persona-driven intents and skill-grounded operations, paired with realistic mock workspaces and hybrid verification mechanisms. We then train a family of capable Claw-style models, termed ClawGym-Agents, through supervised fine-tuning on black-box rollout trajectories, and further…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

  • Claw agents
  • scalable frameworks
  • machine learning
  • reinforcement learning
  • task synthesis
  • evaluation benchmarks

Keywords

  • ClawGym
  • Claw agents
  • machine learning
  • reinforcement learning
  • task synthesis
  • evaluation benchmarks
  • dataset
  • agent training

Mentioned in this episode

Organizations: ClawGym, Arxiv

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