Further training of an existing base model on a large, code-heavy data mix before reinforcement learning starts, to deepen its coding knowledge. For Composer 2, Cursor spent most of the compute on 32K-token sequences, extended to 256K, and finished with a short fine-tuning phase on targeted coding tasks.