Composer 2

Two-phase training pipeline: The model is trained first with continued pretraining to improve knowledge and latent coding ability, followed by large-scale reinforcement learning to improve end-to-end coding performance. The RL phase targets stronger reasoning, accurate multi-step execution, and coherence on long-horizon realistic coding problems.

Train-in-harness infrastructure: Cursor developed infrastructure to support training in the same harness used by the deployed model, with equivalent tools and structure. Training environments match real problems closely, bridging the gap between training-time and deployment-time behavior.

New internal benchmark: To measure the model on increasingly difficult tasks, the team introduces CursorBench, a benchmark derived from real software engineering problems in large codebases, including their own. Composer 2 achieves a major improvement in accuracy over previous Composer models on this benchmark.

Frontier-level performance: On public benchmarks, the model scores 61.7 on Terminal-Bench and 73.7 on SWE-bench Multilingual in Cursor’s harness, comparable to state-of-the-art systems. The report demonstrates that domain-specialized training with RL can produce models competitive with much larger general-purpose systems.