Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling
In the unsupervised self-evolution of Multimodal Large Language Models, the quality of feedback signals during post-training is pivotal for stable and effective learning. However, existing self-evolution methods predominantly rely on majority voting to select the most frequent output as the pseudo-golden answer, which may stem from the model's intrinsic biases rather than guaranteeing the objective correctness of the reasoning paths. To counteract the degradation, we propose Continuous Softened Retracing reSampling (CSRS) in MLLM self-evolution. Specifically, we introduce a Retracing Re-inference Mechanism (RRM) that the model re-inferences from anchor points to expand the exploration of ...
| Authors | Yunyao Yu, Zhengxian Wu, Zhuohong Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 April 2026 |
| Last updated at source | 8 April 2026 |
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