A new paper introduces ClawGym II, a framework designed to train AI agents by leveraging existing tools like OpenClaw and Claude Code. This system captures interactions with these tools, organizing them into prefix trees to optimize models using Proximal Policy Optimization (PPO) and GRPO. The research demonstrates significant improvements in Pass@1 scores for the Qwen3-30A3B model, with gains of 9.98 points using OpenClaw and 14.81 points using Claude Code. Furthermore, the study explores mix-harness training, where a single model is optimized by multiple heterogeneous harnesses to enhance generalization across different execution systems. AI
IMPACT This research could lead to more capable AI agents that generalize better across different tools and environments.
RANK_REASON The cluster describes a research paper detailing a new framework for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
Read on X — Omar Sanseviero (HF research) →
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