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Reward Uncertainty

topic1 events
papersSEP 10 04:00 UTC

Reward Uncertainty Used to Induce Diverse Behaviour in Reinforcement Learning

A newly updated arXiv paper presents a reinforcement learning approach that moves beyond the usual objective of a single deterministic, reward-maximizing policy by incorporating uncertainty over rewards to generate varied behaviour. The authors argue this diversity is essential for applications like fine-tuning language models and accelerating scientific discovery, where multiple distinct solutions are more useful than one optimized output. The v2 release is cross-listed in both the cs.AI and cs.LG categories.

reinforcement-learning/diversity/research/exploration