Kuan-Chen Chen

VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning

Designing effective reward functions remains a major challenge in reinforcement learning (RL), particularly in open-ended environments where task goals are abstract and difficult to quantify. In this work, we present VLM-AR3L, a framework that leverages Vision-Language Models (VLMs) to provide both