Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning 事件

Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning arXiv:2606.03962v1 Announce Type: cross Abstract: Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applications such as language model fine-tuning or scientific discovery demand diversity. Existing remedies such as entropy regularization or diversity bonuses often require fragile trade-offs that sacrifice performance for stoch