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Detailed walkthrough of Cursor's approach to training Composer 2: using Kimi 2.5 as the base, learning code knowledge through large-scale intermediate training, then large-scale RL to teach the model to write correct code in real environments, and using self-summarization to handle long contexts.
This article delves into the technical details such as asynchronous and sparse methods used in Cursor training Composer 2 model, and provides a comprehensive analysis of the RL infrastructure.
Cursor shared the training methods for its self-developed programming model Composer 2, including large-scale continuous pre-training, long-range reinforcement learning, and an internal benchmark CursorBench, which brings the model's programming performance to a top level.