Information
## Cursor Composer
Cursor Composer is the latest coding model provided by Cursor which uses RL method to fine tune the base open source models. On their benchmarks, the model achieves frontier coding results with generation speed four times faster than similar models.
Our motivation comes from our experience developing Cursor Tab, our custom completion model. We found that often developers want the smartest model that can support interactive use, keeping them in the flow of coding. In our development process, we experimented with a prototype agent model, codenamed Cheetah, to better understand the impact of faster agent models. Composer is a smarter version of this model that keeps coding delightful by being fast enough for an interactive experience.
Composer is a mixture-of-experts (MoE) language model supporting long-context generation and understanding. It is specialized for software engineering through reinforcement learning (RL) in a diverse range of development environments. At each iteration of training, the model is given a problem description and instructed to produce the best response, be it a code edit, a plan, or an informative answer. The model has access to simple tools, like reading and editing files, and also more powerful ones like terminal commands and codebase-wide semantic search.
To measure progress, we constructed an evaluation that measures a model's usefulness to a software developer as faithfully as possible. Our benchmark, Cursor Bench, consists of real agent requests from engineers and researchers at Cursor, along with hand-curated optimal solutions to these requests. The resulting evaluation measures not just the agent’s correctness, but also its adherence to a codebase's existing abstractions and software engineering practices.
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