SWE-RL is a reinforcement learning-based large language model reasoning technique proposed by Facebook Research, aiming to leverage open-source software evolution data to improve model performance in software engineering tasks. This technology optimizes the model's reasoning capabilities through a rule-driven reward mechanism, enabling it to better understand and generate high-quality code. The main advantages of SWE-RL lie in its innovative reinforcement learning approach and effective utilization of open-source data, opening up new possibilities in the field of software engineering. The technology is currently in the research phase and does not yet have a defined commercial pricing, but it shows significant potential in improving development efficiency and code quality.