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Former DeepSeeker and collaborators release new method for training reliable AI agents: RAGEN
RAGEN stands out not just as a technical contribution but as a conceptual step toward more autonomous, reasoning-capable AI agents.
Unlike static tasks like math solving or code generation, RAGEN focuses on multi-turn, interactive settings where agents must adapt, remember, and reason in the face of uncertainty. Built on a custom RL framework called StarPO (State-Thinking-Actions-Reward Policy Optimization), the system explores how LLMs can learn through experience rather than memorization. As AI continues to move toward autonomy, projects like RAGEN help illuminate what it takes to train models that learn not just from data, but from the consequences of their own actions.
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