mlsanityData to 14 Oct 2026

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

arXiv 2607.00483 ↗First posted 1 Jul 2026cs.ROcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 38 of its 48 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

38 of 48 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Real-World Offline Reinforcement Learning from Vision Language Model Feedback8 Nov
  2. 2024FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning2 Jun
  3. 2024RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback6 Feb
  4. 2023Vision-Language Models as a Source of Rewards14 Dec
  5. 2023Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning19 Oct
  6. 2023Towards A Unified Agent with Foundation Models18 Jul
  7. 2023Reinforcement Learning Friendly Vision-Language Model for Minecraft19 Mar

Other references · 31

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  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2017Deep reinforcement learning from human preferencescited by 3,912
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  11. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 571
  12. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 478
19 more
  1. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
  2. 2018Reward learning from human preferences and demonstrations in Ataricited by 316
  3. 2023Language to Rewards for Robotic Skill Synthesiscited by 312
  4. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 302
  5. 2023Reward Design with Language Modelscited by 261
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  14. 2022Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?cited by 70
  15. 2022Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexitycited by 62
  16. 2023Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Modelscited by 22
  17. 2023LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teacherscited by 14
  18. 2025VLP: Vision-Language Preference Learning for Embodied Manipulationcited by 8
  19. 2025Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learningcited by 7

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