OpenClaw RL Training Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection. OpenClaw RL is a fully asynchronous reinforcement learning framework that converts live multi turn conversations into training signals for personalized AI agents. It wraps a self hosted model as an OpenAI compatible API via [OpenClaw](https://openclaw.ai), intercepts conversations, and continuously optimizes the policy in the background without interrupting usage. It also supports scalable RL for terminal, GUI, SWE, and tool call agents. Architecture Overview Four independent async loops that never block each other: 1. Agent Serving — OpenClaw compatible API serving rollouts 2. Rollout Collection — Captures multi turn conversations as training trajectories 3. PRM/Judge Evaluation — Scores turns using next state feedback (majority voting optional) 4. Policy Training — GRPO/OPD/Combine training via [slime](https://github.com/THUDM/slime) or [Tinker](https://thinkingmachines.ai/tinker/) Installation Project Structure Three Learning Paradigms 1. Binary RL (GRPO) A Process Reward Model scores each turn from next state feedback. Uses GRPO advantage estimation with PPO style clipped surrogate loss. 2. On Policy Distillation (OPD) When next state reveals useful hindsight, a judge extracts a textual hint to augment the prompt, creating an enhanced teacher. Token level log probability gap becomes a directional advantage signal. 3. Combination Method (Recommended) Merges Binary RL scalar supervision with OPD token level directional signal. Strongest and most robust optimization. Quick Start — Personal Agent (Track 1) Binary RL Launch Script OPD Launch Script Combination Method (One Line) Configuration — Key Environment Variables LoRA Training Custom Loss / Rollout Functions (Plugin API) The slime framework exposes extension points without modifying core code: Example Custom Loss (TypeScript style config, Python implementation) Example Custom Reward Model Deploying on Tinker (Cloud) Track 2 — General Agentic RL Terminal Agent RL GUI Agent RL Tool Call Agent RL SWE Agent RL Data Format — Conversation Trajectories OpenClaw RL automatically classifies API messages. Manual format for custom data: main turns : Multi turn interactions that form training trajectories side turns : Non trainable system/utility turns excluded from training OpenClaw API Server Setup Majority Voting for Robust PRM Scoring Adding a New Method (Contribution Pattern) Common Patterns Monitor Training Progress Resume from Checkpoint Evaluate Trained Checkpoints Troubleshooting Out of GPU memory during rollout + training: Async loop falling behind (buffer overflow): PRM scores all near 0.5 (reward collapse): Verify next state fields contain meaningful feedback signals Check judge model prompt template matches expected format Try increasing majority vote N: majority vote n 7 SGLang server not starting: Ray job submission fails: Key References [Technical Report (arXiv)](https://arxiv.org/abs/2603.10165) [OpenClaw Plugin](https://openclaw.ai) [Slime Training Framework](https://github.com/THUDM/slime) [Tinker Cloud Platform](https://thinkingmachines.ai/tinker/) [SDFT Paper](https://arxiv.org/abs/2601.19897) — integrated in openclaw opd [SDPO Paper](https://arxiv.org/abs/2601.20802) — integrated in openclaw opd

openclaw-rl-training