Listener-Rewarded Thinking in VLMs for Image Preferences

• URL: http://arxiv.org/abs/2506.22832v2
• Date: Tue, 01 Jul 2025 13:53:50 GMT
• Title: Listener-Rewarded Thinking in VLMs for Image Preferences
• Authors: Alexander Gambashidze, Li Pengyi, Matvey Skripkin, Andrey Galichin, Anton Gusarov, Konstantin Sobolev, Andrey Kuznetsov, Ivan Oseledets,
• Abstract summary: We introduce a listener-augmented GRPO framework to train visual reward models.<n>Our listener-shaped reward scheme achieves best accuracy on the ImageReward benchmark.<n>These results demonstrate that listener-based rewards provide a scalable, data-efficient path to aligning vision-language models with nuanced human preferences.
• Score: 38.07052490646366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training robust and generalizable reward models for human visual preferences is essential for aligning text-to-image and text-to-video generative models with human intent. However, current reward models often fail to generalize, and supervised fine-tuning leads to memorization, demanding complex annotation pipelines. While reinforcement learning (RL), specifically Group Relative Policy Optimization (GRPO), improves generalization, we uncover a key failure mode: a significant drop in reasoning accuracy occurs when a model's reasoning trace contradicts that of an independent, frozen vision-language model ("listener") evaluating the same output. To address this, we introduce a listener-augmented GRPO framework. Here, the listener re-evaluates the reasoner's chain-of-thought to provide a dense, calibrated confidence score, shaping the RL reward signal. This encourages the reasoner not only to answer correctly, but to produce explanations that are persuasive to an independent model. Our listener-shaped reward scheme achieves best accuracy on the ImageReward benchmark (67.4%), significantly improves out-of-distribution (OOD) performance on a large-scale human preference dataset (1.2M votes, up to +6% over naive reasoner), and reduces reasoning contradictions compared to strong GRPO and SFT baselines. These results demonstrate that listener-based rewards provide a scalable, data-efficient path to aligning vision-language models with nuanced human preferences. We will release our reasoning model here: https://huggingface.co/alexgambashidze/qwen2.5vl_image_preference_reasoner.

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