🚀 AReaL v1.0 is here! Evolve your 🦞#OpenClaw agents (or any agent) with RL—zero changes to agent required.
We add a transparent proxy that shadows your agent's base_url, capturing all agent–LLM interactions for RL training. Just swap the URL and go.
✅ One-click agentic RL for any existing agent—no modifications needed
✅ Full #opencode recipe: training code, data, infra, and models all open
✅ Archon Engine: PyTorch-native 5D parallelism via pure PyTorch; uv sync and go (zero manual compilation)
✅ torch.compile by default: instant 10% performance boost out of the box
📊 SOTA on tau2Bench: 73.0% pass@1 (Airline) / 98.3% (Telecom)
#opensource #inclusionAI #RL
📄 Paper: https://t.co/wgAws6D9jR
🐙 GitHub: https://t.co/O1Bw2KwjZr
Train your OpenClaw agent:
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Why it matters
Reinforcement learning on an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → you already run, with no code changes: point it at the proxy and trajectories are collected. Training code, data and models are open, with tau2Bench numbers to check.
