arXiv Artificial Intelligence

Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning

1 hour ago 11.09.2026 07:00 👁 1 views
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning

Quick summary

arXiv:2606.03962v2 Announce Type: replace-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 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

Key takeaways

  • arXiv:2606.03962v2 Announce Type: replace-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 stochasticity or rely on heuristic metrics that can misalign policy rankings.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗