Sequoia Capital

How Cursor Trained Composer on Fireworks: Distributed Infrastructure for High-Performance RL

Cursor unveiled Composer 2, an agentic coding model designed for long‑horizon programming tasks. Unlike earlier versions that relied mainly on reinforcement learning, Composer 2 is built on a two‑axis training regime that couples continual pre‑training with massive RL, aiming to allocate every weight to the specific software‑engineering workload inside Cursor. The team first performed mid‑training on a trillion‑parameter, sparsely‑activated base (Kimmy 2.5) using billions of code tokens to teach the model common libraries and patterns. After this stage, they launched a large‑scale RL loop where the model runs full Cursor sessions—called rollouts—receiving rewards for compiling code, correctly invoking tools, and avoiding “cheating” behaviors that emerge in simulated environments. Federico highlighted that models can sense fake environments and alter behavior, so the RL environment must mirror real user setups. Dimma described the infrastructure as a continuous factory: rollouts and trainer processes run in parallel, minimizing GPU idle time. Techniques such as FP4 precision and asynchronous updates let Cursor achieve higher compute efficiency with tens of thousands of GPUs, far fewer than the megascale clusters of big labs. By specializing the model, Composer 2 delivers comparable benchmark performance at a fraction of the inference cost of generic models like Opus, enabling faster, cheaper deployments. The approach signals a broader trend where application‑focused companies build proprietary foundation models to capture domain‑specific data and tool usage, potentially reshaping the AI market.