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

Kuan-Chen Chen; Winston Chen; Wei-Fang Sun; Min-Chun Hu

IJCAI 20260 citations

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

Kuan-Chen Chen, Winston Chen, Wei-Fang Sun, Min-Chun Hu

Abstract

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 absolute and relative rewards for RL. VLM-AR3L interprets an agent’s visual observations in the context of a natural language task goal, and learns both absolute and relative rewards from VLM-generated preference labels. The absolute reward model predicts scalar evaluations for individual states, while the relative reward model compares consecutive observations to infer progress or regression toward the task goal. Their integration combines the stability of state-based evaluation with the robustness of comparative supervision. We evaluate VLM-AR3L across benchmarks spanning classic control, manipulation, and open-world embodied tasks, with a particular focus on Minecraft given its visual complexity and long-horizon decision-making requirements. Experimental results show that VLM-AR3L consistently outperforms prior VLM-based reward learning methods. Videos and code are available on the project website: https://vlm-ar3l.github.io/.

Computer Vision: Embodied vision: Active agents, simulationMachine Learning: Foundation modelsMachine Learning: Learning preferences or rankingsMachine Learning: Partially observable reinforcement learning and POMDPsMachine Learning: Reinforcement learning

BibTeX

@inproceedings{ijcai2026_vlmar3lvisionlan,
  title = {VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning},
  author = {Kuan-Chen Chen and Winston Chen and Wei-Fang Sun and Min-Chun Hu},
  booktitle = {IJCAI 2026},
  year = {2026}
}