构建可扩展的机械爪智能体开发框架，支持全流程训练与评估
ClawGym: A Scalable Framework for Building Effective Claw Agents
- 基于角色意图与技能生成13.5K个合成任务，配真实模拟工作区
- 通过监督微调和轻量强化学习训练出高效智能体
- 提供200个经人机审核校准的评测实例，适合智能体开发者
机械爪类环境支持对本地文件、工具及持久化工作区的多步操作流程。然而，这类环境的可扩展开发受限于缺乏系统性框架，尤其在合成可验证训练数据并整合进训练与评估方面。为此，我们提出ClawGym，一个支持机械爪类个人智能体全生命周期开发的可扩展框架。具体而言，我们构建了ClawGym-SynData，一个由角色驱动意图与技能基础操作生成的13.5K个过滤后任务的多样化数据集，配有真实模拟工作区和混合验证机制。随后，我们在黑箱回放轨迹上通过监督微调训练出一系列能力强大的机械爪模型（ClawGym-Agents），并探索了一种轻量级强化学习流程，通过任务级沙盒并行化回放。为支持可靠评估，我们进一步构建了ClawGym-Bench，一个200个实例的基准测试集，通过自动化过滤与人工-大模型联合审查进行校准。相关资源已发布于https://github.com/ClawGym。
原文摘要 · Abstract (English)
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 explore reinforcement learning via a lightweight pipeline that parallelizes rollouts across per-task sandboxes. To support reliable evaluation, we further construct ClawGym-Bench, a benchmark of 200 instances calibrated through automated filtering and human-LLM review. Relevant resources have been released at https://github.com/ClawGym.
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