A technical report on Composer 2

Composer 2, a coding model for agentic software engineering, is detailed in a technical report that outlines its training process and infrastructure. The model undergoes two phases of training: continued pretraining on a code-focused data mix to enhance the base model's coding knowledge and large-scale reinforcement learning (RL) aimed at improving agent performance in realistic Cursor sessions. A notable improvement in both average and best-of-K performance indicates that Composer 2 learns new solution paths rather than relying solely on known ones. Real-world evaluation is conducted using CursorBench, a benchmark built from genuine coding tasks, which ensures alignment with practical problems faced by developers. Composer 2 achieves a 61.3 score on CursorBench, a 37% improvement over its predecessor, and performs competitively on public benchmarks while maintaining lower inference costs. The training required extensive infrastructure development, including custom low-precision kernels, asynchronous RL pipelines, and the Anyrun compute platform, with a focus on efficient and scalable model training. The report also includes details on weight synchronization, fault tolerance, and environment fidelity, acknowledging the contributions of collaboration partners and the broader open-source community.

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