做RL研究或语言模型微调的团队，如果正为多样性-性能权衡头疼，这篇论文给出了一个理论干净的新框架——把奖励不确定性当作多样性来源，不用额外调参。值得细读。
经典强化学习追求确定性策略以最大化标量奖励期望，但在语言模型微调或科学发现等现代应用中，多样性至关重要。现有方法如熵正则化或多样性奖励常需脆弱权衡，牺牲性能换取随机性。本文提出将奖励函数视为分布而非标量，通过非线性的动作集目标函数，使校准的行为多样性自然涌现，且不牺牲期望奖励。在上下文赌博机设定下，推导了原则性的梯度估计器，证明该框架泛化了策略梯度与动作集方法。实验表明，该方法为需要行为广度的复杂RL任务提供了稳健的理论替代方案。
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
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 stochasticity or rely on heuristic metrics that can misalign policy rankings. We argue that diversity is more naturally understood as the rational response to uncertainty in the reward. When the reward function is not perfectly known--as is the case with ambiguous preferences or imperfect reward models--committing to a single action can be sub-optimal. Building on this, we propose a fundamental reformulation of the RL objective by replacing the scalar reward with a distribution over reward functions, and applying a non-linear objective over sets of actions. The result is a framework in which calibrated behavioural diversity emerges naturally, remains controllable through the reward function distribution, and is obtained without sacrificing expected reward. Focusing on the contextual bandit setting, we derive a principled gradient estimator for this objective and prove that our formulation naturally generalizes both vanilla policy gradient and more recently developed action-set approaches. Our empirical results demonstrate that this framework offers a robust and theoretically grounded alternative for complex RL tasks where the traditional formulation of the problem fails to induce the desired breadth of agent behaviour.