{"ID":6538269,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10966","arxiv_id":"2607.10966","title":"SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning","abstract":"We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning. For each query, the model proposes an answer using the same weights, and issues a binary self-verdict (Yes/No). A 'No' triggers a second-chance rethink; a 'Yes,' or a turn cap, finalizes the output for computing the outcome-based reward. SVR-R1 is implemented with GRPO and an asynchronous multi-turn rollout framework and needs no external supervision or auxiliary critics. We evaluate SVR-R1 on vision-language reasoning benchmarks and show that it improves accuracy by a large margin over strong standard GRPO baselines. Training dynamics show decreasing reliance on verification-fewer verification turns, yet higher test accuracy-indicating that the gap between verification and generation narrows as the policy internalizes self-correction and chooses the most confident answer via our framework. SVR-R1 bridges the less explored intersection of inference-time self-refinement and RL training for VLMs, offering a simple yet effective recipe for bootstrapping multimodal reasoning. We will open-source \\textbf{SVR-R1} to facilitate future research in VLMs.","short_abstract":"We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning. For each query, the model proposes an answer using the same weights, and issues a binary self-verdict (Yes/No). A 'No' triggers a second-chance rethink; a 'Yes,'...","url_abs":"https://arxiv.org/abs/2607.10966","url_pdf":"https://arxiv.org/pdf/2607.10966v1","authors":"[\"Mingyuan Wu\",\"Jingcheng Yang\",\"Shengyi Qian\",\"Xudong Wang\",\"Jize Jiang\",\"Qifan Wang\",\"Aashu Singh\",\"Khoi Pham\",\"Fei Liu\",\"Zhaolun Su\",\"Zhuokai Zhao\",\"Klara Nahrstedt\",\"Jianyu Wang\",\"Hanchao Yu\"]","published":"2026-07-13T00:21:30Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}