ClawGym II

If you want to train agents inside the harness they already run in, this is the black-box version of that idea. ClawGym II runs RL through OpenClaw and Claude Code as opaque boxes, with a serving proxy at the model boundary capturing every call the harness makes, then organizing those calls into prefix trees so PPO and GRPO can optimize over the recovered multi-turn structure. Qwen3-30A3B gains 9.98 points of Pass@1 through OpenClaw and 14.81 through Claude Code, stable across 200 to 400 optimization steps. Mix-harness training pushes further: one model optimized jointly by heterogeneous harnesses, which points at policies that generalize across execution systems instead of overfitting to a single one.