SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning
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 dynam...
| Authors | Mingyuan Wu, Jingcheng Yang, Shengyi Qian et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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