mll-lab-nu/RAGEN

Health v2 · maintenance only

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Adoption not part of the score

2778 stars · 227 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RAGEN is a Python framework for training reasoning LLM agents with multi-turn reinforcement learning using the StarPO algorithm. It also provides diagnostics for studying failure modes in agentic RL, such as the Echo Trap and reasoning/template collapse.

Use cases

• train llm agents with multi-turn reinforcement learning
• apply starpo to trajectory-level agent training
• diagnose reasoning collapse in agent rl training
• detect template collapse where outputs ignore inputs
• research failure modes in agentic reinforcement learning
• run ppo or grpo with snr-adaptive prompt filtering
• reproduce ragen paper experiments

When to choose

• you need to RL-train multi-turn LLM agents with trajectory-level rewards
• you want research-grade diagnostics for agent RL stability
• you want to reproduce or extend the RAGEN/StarPO papers

When to avoid

• you only need inference-time agent orchestration without RL training
• you need a production-ready, turnkey RLHF pipeline for chat models
• you lack GPU resources for large-scale LLM training

Facets

framework · maturity active

reinforcement-learning llm-training agent-framework machine-learning benchmarking reinforcement-learning large-language-models machine-learning python agent-rl starpo multi-turn-rl reasoning-collapse llm-agents rl-training diagnostics research-framework ai-agents research gpu linux docker

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