Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs
Deploying AI-based visual inspection in manufacturing is hard because requirements change often, new defect types appear, and large labeled datasets are rarely available. We propose answer-conditioned chain-of-thought (CoT) distillation for rapidly adapting small vision-language models (VLMs) to new industrial tasks using minimal labeled data. A frontier VLM receives each training image along with its correct label and generates a justified visual explanation. A 3B-parameter model is then fine-tuned on these reasoning-augmented examples via LoRA. By conditioning on correct answers, we ensure all training reasoning is directed toward the correct conclusion, which is critical because fronti...
| Authors | Shubham Rao |
|---|---|
| Primary category | cs.CV |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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