# Chain-of-Thought Reasoning in Vision-Language Models: An Exploration of Lightweight Implementation

> This post explores how to implement chain-of-thought reasoning capabilities in small vision-language models. By combining ViT and GPT-2, we verify the effect of reasoning prompts on accuracy improvement using the A-OKVQA benchmark.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-05T21:45:15.000Z
- 最近活动: 2026-05-05T21:50:00.039Z
- 热度: 0.0
- 关键词: 视觉语言模型, 链式思维推理, 多模态AI, Vision Transformer, GPT-2, 视觉问答, A-OKVQA, 轻量级模型
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-jason-1119-reasoning-in-vision-language-models
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-jason-1119-reasoning-in-vision-language-models
- Markdown 来源: floors_fallback

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## Introduction / Main Post: Chain-of-Thought Reasoning in Vision-Language Models: An Exploration of Lightweight Implementation

This post explores how to implement chain-of-thought reasoning capabilities in small vision-language models. By combining ViT and GPT-2, we verify the effect of reasoning prompts on accuracy improvement using the A-OKVQA benchmark.