OpenClaw-RL
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What is this?
OpenClaw-RL is a fully asynchronous reinforcement learning framework that turns everyday conversations with an agent into training signals, letting you train a personalized OpenClaw agent just by talking to it, or train general agents for terminal, GUI, SWE, and tool-call tasks.
OpenClaw-RL is a fully asynchronous reinforcement learning framework that turns everyday conversations with an agent into training signals, letting you train a personalized OpenClaw agent just by talking to it, or train general agents for terminal, GUI, SWE, and tool-call tasks.Is this a ready-made agent or a framework I plug my own agent into?It is a training framework, not a standalone agent: it wraps your self-hosted model behind an OpenAI-compatible API and runs four independent async loops (serving, rollout collection, judging, policy training) on top of the Slime base RL framework.
It is a training framework, not a standalone agent: it wraps your self-hosted model behind an OpenAI-compatible API and runs four independent async loops (serving, rollout collection, judging, policy training) on top of the Slime base RL framework.How do I get it training my agent?Set up the environment per ./instructions/README.md (CUDA 12.9, Python 3.12, Slime), start the RL server so your model is served as an OpenAI-compatible API, then add that endpoint as a provider in your OpenClaw's openclaw.json — the server collects your conversations and trains in the background as you chat.
Set up the environment per ./instructions/README.md (CUDA 12.9, Python 3.12, Slime), start the RL server so your model is served as an OpenAI-compatible API, then add that endpoint as a provider in your OpenClaw's openclaw.json — the server collects your conversations and trains in the background as you chat.Do I need a full GPU cluster to run this?By default yes — 8 GPUs (configurable via NUM_GPUS, ACTOR_GPUS, ROLLOUT_GPUS, PRM_GPUS), plus CUDA 12.9, Python 3.12, and the Slime RL framework; alternatively you can skip local GPUs and run via cloud backends like Tinker or Fireworks AI.
By default yes — 8 GPUs (configurable via NUM_GPUS, ACTOR_GPUS, ROLLOUT_GPUS, PRM_GPUS), plus CUDA 12.9, Python 3.12, and the Slime RL framework; alternatively you can skip local GPUs and run via cloud backends like Tinker or Fireworks AI.What's the catch with the one-line setup?The default personal-agent setup assumes serious hardware (8 GPUs) or a paid cloud backend; it's also a research-stage project (v1 released Feb 2026) built around a specific base framework (Slime), so you're tied to that stack for training.
The default personal-agent setup assumes serious hardware (8 GPUs) or a paid cloud backend; it's also a research-stage project (v1 released Feb 2026) built around a specific base framework (Slime), so you're tied to that stack for training.Does my conversation data leave my machine during training?No — the policy model, judge/PRM, and trainer all run on your own infrastructure, and no third-party model API is required.
No — the policy model, judge/PRM, and trainer all run on your own infrastructure, and no third-party model API is required.What's the difference between Binary RL, OPD, and the Hybrid method?Binary RL (GRPO) scores each turn with a reward model and applies PPO-style GRPO updates; OPD has a judge model craft a hindsight hint that creates an improved teacher response, and the token-level gap between teacher and student becomes the training signal; the Hybrid method combines both signals for stronger optimization than either alone.
Binary RL (GRPO) scores each turn with a reward model and applies PPO-style GRPO updates; OPD has a judge model craft a hindsight hint that creates an improved teacher response, and the token-level gap between teacher and student becomes the training signal; the Hybrid method combines both signals for stronger optimization than either alone.What is OpenClaw in this context?OpenClaw is the self-hosted AI agent being trained — you install an extension so your own OpenClaw routes its traffic through the RL server, which then learns from every conversation.
OpenClaw is the self-hosted AI agent being trained — you install an extension so your own OpenClaw routes its traffic through the RL server, which then learns from every conversation.Which agent settings beyond personal chat does it support?Track 2 extends the same async RL backbone to terminal agents (shell sandboxes), GUI agents (screen state/accessibility tree), SWE agents (code repos plus test suites), and tool-call agents (API/function execution).
Track 2 extends the same async RL backbone to terminal agents (shell sandboxes), GUI agents (screen state/accessibility tree), SWE agents (code repos plus test suites), and tool-call agents (API/function execution).Has this been validated beyond the repo itself?Yes — the team published a technical report that ranked #1 on Hugging Face's Daily Papers, and the framework now supports Qwen3.5 models (4B/9B/27B, both text and multi-modal).
Yes — the team published a technical report that ranked #1 on Hugging Face's Daily Papers, and the framework now supports Qwen3.5 models (4B/9B/27B, both text and multi-modal).Which topics is it in?openclaw-skills, grpo, llm-training, rlhf, sglang, coding, lora, async, openclaw, agent-framework, reinforcement-learning, agent-memory
openclaw-skills, grpo, llm-training, rlhf, sglang, coding, lora, async, openclaw, agent-framework, reinforcement-learning, agent-memory