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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.

arXiv Diverse Behaviour Language Model Fine-tuning Reward Uncertainty reinforcement-learning scientific-discovery

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIUsing Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning ↗SEP 10 04:00 UTC

arXiv cs.LGUsing Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning ↗SEP 10 04:00 UTC