Researchers have identified a critical issue in training large language model agents using dense prediction rewards, particularly when combined with the GRPO algorithm. This method, intended to provide step-by-step supervision, can lead to a phenomenon dubbed the "dark room" pathology, causing agents to enter degenerate states where task success plummets despite high prediction accuracy. The study found that removing GRPO's group normalization resolves this catastrophic failure, suggesting that the normalization amplifies within-group variance in a way that destabilizes the training process. The research proposes a variance-profile criterion to predict and avoid such collapses, indicating that auxiliary-loss channels may be more effective than reward channels for dense signal delivery. AI
IMPACT Identifies a critical training failure mode for LLM agents, potentially impacting future reinforcement learning approaches.
RANK_REASON Academic paper detailing a novel failure mode in LLM agent training. [lever_c_demoted from research: ic=1 ai=1.0]
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