The paper addresses uneven performance across task categories in software-engineering agents by training category-specific expert models via reinforcement learning with a ‘Refresh-Repair-Expand’ mechanism that reuses successful trajectories and adapts task selection. The experts are then consolidated into a single deployable model via multi-teacher distillation with reward extrapolation. The resulting model reaches roughly 58-59% resolution on benchmark datasets, a significant improvement over baselines.
