Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs
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 ASTOR, a multi-tASk code reinforcement learning framework via uTility-driven coORdination. Centered on task utility, a signal capturing each task learning potential and cross-task synergy, ASTOR compri...
| Authors | Yujia Chen, Yang Ye, Xiao Chu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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