Shypd.aiThe largest open index of AI toolsRAGEN is an AI Agents & Automation tool that leverages reinforcement learning to train LLM reasoning agents in interactive, stochastic environments. It provides a flexible framework for optimizing reasoning processes and actions.
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RAGEN (Reasoning AGENT) is a flexible reinforcement learning framework designed for training reasoning agents, particularly Large Language Models (LLMs), in interactive and stochastic environments. It introduces StarPO (State-Thinking-Actions-Reward Policy Optimization), a unified RL framework that supports multi-turn, trajectory-level agent training with fine-grained control over reasoning processes, reward assignment, and prompt-rollout structures. RAGEN-2, the latest iteration, includes SNR-Adaptive Filtering to mitigate noisy gradient updates and reasoning collapse diagnostics to detect and monitor template collapse during training. The framework is compatible with Gym environments and offers 10 built-in environments for diverse testing. It's ideal for researchers and developers focused on advancing the capabilities and stability of LLM-based agents.
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