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cs.LG2026
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Anthony GX-Chen, Ankit Anand, Gheorghe Comanici +7
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 fin…
cs.LG2024
Code as Reward: Empowering Reinforcement Learning with VLMs
David Venuto, Sami Nur Islam, Martin Klissarov +3
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion. In t…