Trinity: Self-Evolving Vision-Language Models with a Self-Verifier
Self-evolving vision-language models (VLMs), a form of self-improvement in which a model generates its own training data from unlabeled images, are a promising route toward agents that expand their reasoning capability in an unsupervised manner, without relying on ever-larger annotation budgets. Existing methods pair a Questioner that proposes problems with a Solver that answers them, but reward both roles mainly by agreement among sampled answers. Agreement is a weak proxy for truth: it cannot tell whether a question is grounded in the image, whether the proposed reference answer is right, or whether a confident majority is wrong in the same way. We present Trinity, in which one VLM play...
| Authors | Youngwan Lee, Yong-Ju Lee, Sung Ju Hwang |
|---|---|
| Primary category | cs.AI |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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