ASTOR: Multi-Task Code Reinforcement Learning via Utility-Driven Coordination

Reinforcement learning (RL) with verifiable rewards has proven effective at post-training LLMs for coding, yet deploying separate task-specific specialists incurs costs that scale with the number of tasks, motivating a unified multi-task RL (MTRL) approach. However, existing MTRL methods treat all coding tasks uniformly, relying on fixed data curricula under a shared optimization strategy, ultimately limiting the effectiveness of multi-task training. To address these limitations, we propose \tool, a multi-tASk code reinforcement learning framework via uTility-driven coORdination. Centered on task utility, a signal capturing each task's learning potential and cross-task synergy, \tool comprises two coupled modules: 1) \moduleone module hierarchically allocates training budget and prioritizes informative prompts, steering training toward the most valuable data; and 2) \moduletwo module dynamically scales per-task KL regularization, matching update constraints to each task's current training state. Experiments on two widely-used LLMs across four representative coding tasks demonstrate that \tool consistently improves a single model across all tasks, outperforming the best task-specific specialist by 9.0%–9.5% and surpassing the strongest MTRL baseline by 7.5%–12.8%.