OpenClaw-RL：让 LLM Agent 在对话中进化的在线强化学习框架

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OpenClaw-RL: An Online Reinforcement Learning Framework for Evolving LLM Agents in Conversation ===============================================================================================

OpenClaw-RL: An Online Reinforcement Learning Framework for Evolving LLM Agents in Conversation ===============================================================================================  ### meng shao

@shao__meng

OpenClaw-RL: LLM-based Agent 的在线强化学习框架，把用户对话中的不满、重问、纠正、满意等自然反馈，自动转化为 RL 训练信号，让你的 OpenClaw “越用越聪明”

关键技术亮点

• 完全异步4组件架构（最核心创新）

Agent 服务 ↔ 轨迹收集 ↔ 评估（PRM/判别器） ↔ 策略训练 → 四者解耦，后台持续优化，前台对话零卡顿。

• 三种学习范式（可自由组合）

· Binary RL (GRPO)：把下一轮用户/环境反馈当成标量奖励（好/坏），用 PPO 式优化。

· On-Policy Distillation (OPD)：从反馈中提取“后见之明”提示，计算 token 级方向性优势（SDFT/SDPO 实现）。

· Combination (Hybrid)：标量奖励 + token 级信号联合优化，效果最强（官方推荐）。

• 完全自托管 & 隐私优先

所有训练/推理都在本地跑，无需云 API。

• 支持的真实世界 Agent 场景（非模拟器）

· 终端操作（shell sandbox）

· GUI 自动化（屏幕+无障碍树）

· SWE（代码仓库+测试）

· 工具调用（function calling）

开源地址 github.com/Gen-Verse/Open…Show More

!Image 3: Tweet image

!Image 4: Sumanth

#### Sumanth

@Sumanth_077 · 18h ago

Train your OpenClaw agent by just talking to it!

OpenClaw-RL is a reinforcement learning framework that turns everyday conversations into training signals for personalized AI agents.

Most RL systems for LLMs assume batch-mode training with pre-collected datasets. You label data manually, train offline, deploy, and hope it works.

OpenClaw-RL wraps your self-hosted model as an OpenAI-compatible API through OpenClaw, intercepts live conversations, and continuously optimizes the policy in the background while you use it.

How it works:

Four independent async loops run simultaneously - agent serving, rollout collection, reward judging, and policy training. The model serves your requests while training happens in the background.

No manual labeling. The system automatically classifies messages, uses the next user message as a signal, runs reward evaluation asynchronously, and submits samples to the trainer.

Two learning modes:

• Binary RL (GRPO) - Reward model scores each turn as good/bad/neutral. Works with thumbs up/down or environment success/failure.

• On-Policy Distillation (OPD) - Extracts textual hints from feedback like "you should have checked the file first." Creates an enhanced teacher for token-level training.

Everything runs on your infrastructure. No external API keys required. Conversation data stays local.

It's 100% open source

Link to OpenClaw-RL in comments!Show More

!Image 5: Tweet image

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4,475 Views  meng shao @shao__meng

One Sentence Summary

OpenClaw-RL is an open-source online reinforcement learning framework that continuously optimizes LLM Agents in the background by converting natural user feedback into training signals.

Summary

This tweet introduces OpenClaw-RL, an innovative framework designed to address the challenge of LLM Agent training being disconnected from real-world usage scenarios. Its core highlight is a fully asynchronous four-component architecture (serving, trajectory collection, evaluation, policy training), which enables seamless background optimization during user conversations. Technically, it offers three paradigms: Binary RL (GRPO), On-Policy Distillation (OPD), and a Hybrid mode. It supports real-world Agent scenarios like terminal operations, GUI automation, and code repositories, and emphasizes local, private deployment for enhanced data privacy.

AI Score

82

Influence Score 10

Published At Today

Language

Chinese

Tags

OpenClaw-RL

Reinforcement Learning

LLM Agent

Online Learning

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