When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL 事件

When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL arXiv:2605.28918v1 Announce Type: cross Abstract: For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging than one-shot generation. We study PPO-trained agents using MiniGrid as core evaluation and MuJoCo as boundary stress test. Our audit finds two dominant one-shot failure modes -- reward flooding and semantic/API