AIBullisharXiv – CS AI · 6h ago6/10
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Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs
Researchers introduce ASTOR, a multi-task reinforcement learning framework that trains a single code LLM across multiple coding tasks more efficiently than task-specific models. By dynamically prioritizing training data and adjusting optimization constraints based on task utility, ASTOR achieves 9.0-9.5% performance gains over specialized models and 7.5-12.8% improvements over existing multi-task approaches.