SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models

Jiahao Xie; Alessio Tonioni; Nathalie Rauschmayr; Federico Tombari; Bernt Schiele

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arXiv · 2604.20705

SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models

Jiahao Xie·Alessio Tonioni·Nathalie Rauschmayr·Federico Tombari·Bernt Schiele

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Abstract

Reinforcement learning (RL) with verifiable rewards (RLVR) has demonstrated the great potential of enhancing the reasoning abilities in multimodal large language models (MLLMs). However, the reliance on language-centric priors and expensive manual annotations prevents MLLMs' intrinsic visual understanding and scalable reward designs. In this work, we introduce SSL-R1, a generic self-supervised RL framework that derives verifiable rewards directly from images. To this end, we revisit self-supervised learning (SSL) in visual domains and reformulate widely-used SSL tasks into a set of verifiable visual puzzles for RL post-training, requiring neither human nor external model supervision. Training MLLMs on these tasks substantially improves their performance on multimodal understanding and reasoning benchmarks, highlighting the potential of leveraging vision-centric self-supervised tasks for MLLM post-training. We think this work will provide useful experience in devising effective self-supervised verifiable rewards to enable RL at scale. Project page: https://github.com/Jiahao000/SSL-R1.

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Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele. 2026-04-22. SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models. https://arxiv.org/abs/2604.20705

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