Improving Reasoning in Vision-Language Models via Perception Verified Self-Training
Achieving human-like reasoning in Vision-Language Models (VLMs) remains a long-standing challenge. Recent approaches leverage Chain-of-Thought (CoT) rationales generated by human annotators or proprietary models, which are costly and difficult to scale. Self-training offers a promising alternative but often suffers from visual hallucinations and language shortcuts because rationales are filtered only by answer correctness without verifying visual perception. We propose a perception-verified self-training framework that enforces visually grounded reasoning. Our method employs a CoT template (caption-reasoning-conclusion) that disentangles perception from reasoning, enabling independent ver...
| Authors | Sourabh Sharma, Sonam Gupta, Sadbhawna |
|---|---|
| Primary category | cs.CV |
| Published | 20 June 2026 |
| Last updated at source | 29 June 2026 |
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