TL;DR
A technical report on Composer 2, a specialized coding model trained through continued pretraining followed by large-scale reinforcement learning on long-horizon software-engineering tasks.
Why it matters
Composer 2 is a concrete example of environment fidelity as a scaling variable: training uses the same tool harness and task structure as deployment, with realistic repositories and multi-step execution rather than isolated code generation.
Key findings
- 01
The RL phase targets end-to-end coding performance, multi-step execution, long-horizon planning, and trajectory coherence.
- 02
The training infrastructure is designed to match the deployed Cursor harness, including its tools and interaction structure.
- 03
The authors report strong results on CursorBench, Terminal-Bench, and SWE-bench Multilingual; comparisons remain sensitive to harness and evaluation setup.
Scaling dimensions
Models, methods & benchmarks
- Models
- Composer 2
- Algorithms
- Large-scale reinforcement learning, Continued pretraining
- Benchmarks
- CursorBench, Terminal-Bench, SWE-bench Multilingual
Topics