Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition and Multi-Reward Policy Optimization
Primary area: foundation or frontier models, including LLMs
Keywords: machine learning, vision-language models, deep learning, reinforceme

Abstract: Vision-Language Models (VLMs) often suffer from visual hallucinations – generating things that are not consistent with visual inputs – and language shortcuts, where they skip the visual part and just rely on text priors. These issues arise because most post-training methods for VLMs rely on simple verifiable answer matching and supervise only final outputs, leaving intermediate visual reasoning without explicit guidance. As a result, VLMs receive sparse visual signals and often learn to prioritize language-based reasoning over visual perception. To mitigate this, some existing methods add visual supervision using human annotations or distilled labels from external large models. However, human annotations are labor-intensive and costly, and because external signals cannot adapt to the evolving policy, they cause distributional shifts that can lead to reward hacking. In this paper, we introduce Vision-SR1, a self-rewarding method that improves visual reasoning without relying on external visual supervisions via reinforcement learning and Multi-Reward Policy Optimization. Vision-SR1 decomposes VLM reasoning into two stages: visual perception and language reasoning. The model is first prompted to produce self-contained visual perceptions that are sufficient to answer the question without referring back the input image. To validate this self-containment, the same VLM model is then re-prompted to perform language reasoning using only the generated perception as input to compute reward. This self-reward is combined with supervision on final outputs, providing a balanced training signal that strengthens both visual perception and language reasoning. In addition, Multi-Reward Policy Optimization separately computes advantages and log probabilities for both the visual reasoning reward and the answer accuracy reward. The method then calculates KL divergence regularization and Actor Loss using the combined sum of these two reward components. Our experiments demonstrate that Vision-SR1 improves visual reasoning, mitigates visual hallucinations, and reduces reliance on language shortcuts across diverse vision-language tasks.

TL;DR: We present multi reward and multi loss objective reinforcement learning training method to improve visual understanding and reduce hallucination.