When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning

ACL ARR 2026 January Submission7732 Authors

Keywords: Unsupervised self-evolving;Multimodal reasoning;Reinforcement Learning

Abstract:

Recent progress in multimodal large language models has led to strong performance on reasoning tasks, but these improvements largely rely on high quality annotated data, which is costly and difficult to scale.To address this, we propose an unsupervised self-evolution training framework for multimodal reasoning that achieves stable performance improvements without using human-annotated answers or external reward models. For each input, we sample multiple reasoning trajectories and jointly model their within group structure.
We use the Actor’s self-consistency signal as a training prior, and introduce a bounded Judge based modulation to continuously reweight trajectories of different quality.We further model the modulated scores as a group level distribution and convert absolute scores into relative advantages within each group, enabling more robust policy updates. Trained with Group Relative Policy Optimization (GRPO) on unlabeled data, our method consistently improves reasoning performance and generalization on five mathematical reasoning benchmarks, including MathVision and DynaMath, offering a scalable path toward self-evolving multimodal models.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision question answering;multi-modal dialogue systems

Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 7732