rLLM is an open-source framework for training language agents using reinforcement learning. The project has released several high-performing models including DeepSWE (32B software engineering agent achieving 59% on SWEBench-Verified), DeepCoder (14B coding model matching o3-mini performance with 60.6% Pass@1 on LiveCodeBench), and DeepScaleR (1.5B model surpassing O1-Preview with 43.1% Pass@1 on AIME). The framework enables developers to build custom agents and environments, train them with RL, and deploy for real-world applications.

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