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

• URL: http://arxiv.org/abs/2607.00483v2
• Date: Thu, 02 Jul 2026 01:53:18 GMT
• Title: VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning
• Abstract summary: We present VLM-AR3L, a framework that learns both absolute and relative rewards for reinforcement learning.<n>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.<n>We evaluate VLM-AR3L across benchmarks spanning classic control, manipulation, and open-world embodied tasks, with a particular focus on Minecraft.
• Score: 10.085586108389718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning [12.563247818737294]
  We propose a novel training strategy to adapt vision-language models (VLMs) for object detection and object-state localization.<n>We leverage this training strategy to develop StateVLM, a novel model designed to perceive and learn fine-grained object representations.<n>We show that ARL improves model performance by an average of 1.6% compared to models without ARL.
  arXiv  Detail & Related papers  (2026-05-05T16:19:02Z)
• LIBERO-X: Robustness Litmus for Vision-Language-Action Models [32.29541801424534]
  This work systematically rethinks VLA benchmarking from both evaluation and data perspectives.<n>We introduce LIBERO-X, a benchmark featuring a hierarchical evaluation protocol with progressive difficulty levels targeting three core capabilities.<n> Experiments with representative VLA models reveal significant performance drops under cumulative perturbations.
  arXiv  Detail & Related papers  (2026-02-06T09:59:12Z)
• Vision-aligned Latent Reasoning for Multi-modal Large Language Model [82.26044667101011]
  Vision-aligned Latent Reasoning (VaLR) is a framework that dynamically generates vision-aligned latent tokens before each Chain of Thought reasoning step.<n>VaLR is trained to preserve visual knowledge during reasoning by aligning intermediate embeddings of MLLM with those from vision encoders.
  arXiv  Detail & Related papers  (2026-02-04T12:04:02Z)
• VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models [43.09726338623949]
  Vision-Language-Action (VLA) models integrate pretrained large Vision-Language Models (VLM) into their policy backbone.<n>This paper revisits a fundamental yet seldom systematically studied question: how VLM choice and competence translate to downstream VLA policies performance.<n>We introduce VLM4VLA, a minimal adaptation pipeline that converts general-purpose VLMs into VLA policies using only a small set of new learnable parameters.
  arXiv  Detail & Related papers  (2026-01-06T09:58:24Z)
• Revisiting the Learning Objectives of Vision-Language Reward Models [19.768973349254285]
  Learning generalizable reward functions is a core challenge in embodied intelligence.<n>Recent work leverages contrastive vision language models (VLMs) to obtain dense, domain-agnostic rewards without human supervision.<n>We evaluate recent VLM-based reward models under a unified framework with identical backbones, finetuning data, and evaluation environments.
  arXiv  Detail & Related papers  (2025-12-20T19:50:36Z)
• Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning [124.48672228625821]
  We introduce Vlaser - a Vision-Language-Action Model with synergistic embodied reasoning capability.<n>Vlaser achieves state-of-the-art performance across a range of embodied reasoning benchmarks.<n>Our approach achieves state-of-the-art results on the WidowX benchmark and competitive performance on the Google Robot benchmark.
  arXiv  Detail & Related papers  (2025-10-13T05:51:22Z)
• Agentic Jigsaw Interaction Learning for Enhancing Visual Perception and Reasoning in Vision-Language Models [63.69856480318313]
  AGILE formulates jigsaw solving as an interactive process, enabling the model to progressively engage with the environment.<n>We show that AGILE substantially boosts performance on jigsaw tasks of varying complexity.<n>We also demonstrate strong generalization across 9 general vision tasks, achieving an average improvement of 3.1%.
  arXiv  Detail & Related papers  (2025-10-01T17:58:05Z)
• Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme [36.34443944082215]
  This work introduces a transparent, from-scratch framework forReinforcement learning (RL) in vision-based models (VLMs)<n>It offers a minimal yet functional four-step pipeline validated across multiple models and datasets.<n>In addition, a standardized evaluation scheme is proposed to assess training dynamics and reflective behaviors.
  arXiv  Detail & Related papers  (2025-04-03T13:53:28Z)
• Vision Language Models are In-Context Value Learners [89.29486557646624]
  We present Generative Value Learning (GVL), a universal value function estimator that leverages the world knowledge embedded in vision-language models (VLMs) to predict task progress. Without any robot or task specific training, GVL can in-context zero-shot and few-shot predict effective values for more than 300 distinct real-world tasks.
  arXiv  Detail & Related papers  (2024-11-07T09:17:50Z)
• AutoBench-V: Can Large Vision-Language Models Benchmark Themselves? [65.92331309449015]
  We introduce AutoBench-V, an automated framework for serving evaluation on demand, i.e., benchmarking LVLMs based on specific aspects of model capability.<n>Through an extensive evaluation of nine popular LVLMs across five demanded user inputs, the framework shows effectiveness and reliability.
  arXiv  Detail & Related papers  (2024-10-28T17:55:08Z)
• Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models [73.40350756742231]
  Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning. Despite the volume of new releases, key design decisions around image preprocessing, architecture, and optimization are under-explored.
  arXiv  Detail & Related papers  (2024-02-12T18:21:14Z)
• Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models [61.28463542324576]
  Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can generate human-like outputs. We evaluate existing state-of-the-art VLMs and find that even the best-performing model is unable to demonstrate strong visual reasoning capabilities and consistency. We propose a two-stage training framework aimed at improving both the reasoning performance and consistency of VLMs.
  arXiv  Detail & Related papers  (2023-09-08T17:49:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.