EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent
The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful workspaces. While Agentic Reinforcement Learning (Agentic RL) provides a promising path to optimize these agents, its scaling is heavily bottlenecked by the severe scarcity of interactive training environments. Existing synthetic environments are strictly limited to tool-calling endpoints, rendering them insufficient for accommodating the end-to-end real-world demands of claw-like agents. To bridge this gap, we introduce EnvCraft, an automated framework for synthesizing executable environments and scalable training data. Specifically, Env...
| Authors | Yirong Zeng, Shen You, Jinhang Feng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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