Meet RAGEN Framework: The First Open-Source Reproduction of DeepSeek-R1 for Training Agentic Models via Reinforcement Learning

RAGEN is the first open-source reproduction of DeepSeek-R1, designed to address challenges in training AI agents for multi-step reasoning and real-world tasks. Developed by DeepSeekAI, the framework streamlines agent training and enhances decision-making by using a two-phase approach to ensure stable learning. Tests on the Sokoban puzzle showed that smaller models perform comparably to larger ones, indicating RAGEN's efficiency. This makes the framework valuable for applications like logistics automation and AI assistants, improving reinforcement learning and supporting advancements in general-purpose AI systems.