VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making

• URL: http://arxiv.org/abs/2505.03181v1
• Date: Tue, 06 May 2025 04:51:57 GMT
• Title: VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making
• Authors: Jake Grigsby, Yuke Zhu, Michael Ryoo, Juan Carlos Niebles,
• Abstract summary: Vision-language models (VLMs) extend large language models (LLMs) to multi-modal data.<n>Our work approaches these challenges from an offline-to-online reinforcement learning (RL) perspective.
• Score: 45.02997774119763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research looks to harness the general knowledge and reasoning of large language models (LLMs) into agents that accomplish user-specified goals in interactive environments. Vision-language models (VLMs) extend LLMs to multi-modal data and provide agents with the visual reasoning necessary for new applications in areas such as computer automation. However, agent tasks emphasize skills where accessible open-weight VLMs lag behind their LLM equivalents. For example, VLMs are less capable of following an environment's strict output syntax requirements and are more focused on open-ended question answering. Overcoming these limitations requires supervised fine-tuning (SFT) on task-specific expert demonstrations. Our work approaches these challenges from an offline-to-online reinforcement learning (RL) perspective. RL lets us fine-tune VLMs to agent tasks while learning from the unsuccessful decisions of our own model or more capable (larger) models. We explore an off-policy RL solution that retains the stability and simplicity of the widely used SFT workflow while allowing our agent to self-improve and learn from low-quality datasets. We demonstrate this technique with two open-weight VLMs across three multi-modal agent domains.

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