Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning

Reward uncertainty as a principled mechanism for diverse RL behaviour

A new arXiv preprint proposes replacing the scalar reward in RL with a distribution over reward functions, applying a non-linear objective over sets of actions to induce calibrated behavioural diversity without sacrificing expected reward. The authors derive a principled gradient estimator in the contextual bandit setting and prove the formulation generalizes vanilla policy gradient and action-set approaches. The work is motivated by applications like language model fine-tuning where diversity is desirable but entropy regularization and diversity bonuses introduce fragile trade-offs. Empirical results support the framework as a theoretically grounded alternative to heuristic diversity methods.