Cursor unveiled Composer 2.5, a major upgrade to its AI coding assistant designed to handle longer and more complex coding workflows reliably. The new model introduces targeted reinforcement learning corrections using localized textual feedback, enabling more precise tuning during extended task rollouts. This means Cursor can fine-tune outputs based on specific feedback without requiring complete re-prompting. The model received a 25x increase in synthetic task training alongside improved behavioral calibration, helping it better follow nuanced instructions and maintain consistency throughout long coding sessions. Early feedback suggests significantly stronger performance on extended coding tasks and multiple tool interactions—critical for developers working on substantial features or refactoring. For independent developers and small development teams, this translates to faster feature shipping with fewer iterations. The improved ability to handle extended context and follow complex instructions means less back-and-forth with the AI, allowing developers to maintain momentum. This progress directly addresses one of the biggest pain points in AI-assisted coding: model quality degradation or instruction loss as tasks grow longer.