When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917359081488384 |
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| author | Wu, Zhengxian Shi, Kai Zhang, Chuanrui Liao, Zirui Yang, Jun Yang, Ni Peng, Qiuying Zhang, Luyuan Xu, Hangrui Su, Tianhuang Yang, Zhenyu Lu, Haonan Wang, Haoqian |
| author_facet | Wu, Zhengxian Shi, Kai Zhang, Chuanrui Liao, Zirui Yang, Jun Yang, Ni Peng, Qiuying Zhang, Luyuan Xu, Hangrui Su, Tianhuang Yang, Zhenyu Lu, Haonan Wang, Haoqian |
| contents | 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 or teacher-model distillation, both of which are 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, offering a scalable path toward self-evolving multimodal models. The code are available at https://github.com/OPPO-Mente-Lab/LLM-Self-Judge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21289 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning Wu, Zhengxian Shi, Kai Zhang, Chuanrui Liao, Zirui Yang, Jun Yang, Ni Peng, Qiuying Zhang, Luyuan Xu, Hangrui Su, Tianhuang Yang, Zhenyu Lu, Haonan Wang, Haoqian Computer Vision and Pattern Recognition Artificial Intelligence 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 or teacher-model distillation, both of which are 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, offering a scalable path toward self-evolving multimodal models. The code are available at https://github.com/OPPO-Mente-Lab/LLM-Self-Judge. |
| title | When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.21289 |