ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models
This paper introduces a new dataset called ImageNet-Think-250K, which helps train models that can understand and reason about images and text together. It includes 250,000 images and shows how models think step-by-step before giving answers.
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- 1 The model reveals its step-by-step logical progression instead of mapping inputs directly to outputs.
- 2 We evaluate a model's ability to generate coherent, step-by-step reasoning processes when analyzing visual content.
- 3 We use a diverse suite of metrics to comprehensively evaluate the quality of generated text.
- 4 All metric values are normalized to the range where higher values indicate stronger similarity.
Introduction
The computer vision community explores similar approaches for multimodal reasoning. Thinking models like OpenAI’s o1 series, DeepSeek-R1, GLM-4.1V-Thinking, Kimi-VL-Thinking, and R1-Onevision represent a paradigm shift toward systematic reasoning in VLMs.
These models generate intermediate reasoning steps before final answers to offer insights into multimodal understanding.
Current multimodal datasets focus on input-output mappings without capturing intermediate reasoning steps.
Existing reasoning-focused datasets are often domain-specific or limited in scale, restricting their utility for training robust VLMs.
Prior benchmarks provide valuable reasoning traces but are limited in scale, token coverage, or domain diversity.
Methodology
DocVQA offers 50,000 questions on document images, framing document understanding as an extractive question-answering task. This multi-perspective approach enables analysis of reasoning consistency and development of more robust evaluation metrics.
Study Design
Kimi-VL-A3B-Thinking-2506 may exhibit biases toward systematic, step-by-step analysis.
GLM-4.1V-9B-Thinking might demonstrate biases toward comprehensive analysis.
Results & Findings
Vision Language Models (VLMs) demonstrate remarkable capabilities in understanding and reasoning about visual content. Incorporating explicit reasoning steps like chain-of-thought (CoT) prompting significantly improves large language model performance on complex tasks.
- Vision Language Models (VLMs) demonstrate remarkable capabilities in understanding and reasoning about visual content.
- Incorporating explicit reasoning steps like chain-of-thought (CoT) prompting significantly improves large language model performance on complex tasks.
- Proprietary or limited training datasets create a significant barrier for the broader research community.
- This limitation hinders reasoning model development and makes diagnosing model failures challenging.
- This multi-model dataset generation approach ensures diversity in reasoning patterns and provides comprehensive coverage of analytical perspectives.
This limitation hinders reasoning model development and makes diagnosing model failures challenging.
This limitation makes it difficult to understand model capabilities and failure modes.
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Practical Applications
ImageNet-21k’s original geographic distribution biases may influence visual content representation.
Multimodal Datasets
An overview of various multimodal datasets is provided, detailing their focus areas such as visual question answering, document understanding, and diagram interpretation, while noting their limitations in capturing reasoning processes.
Multimodal Reasoning Datasets
This section discusses the evolution of multimodal reasoning datasets, highlighting their contributions to complex reasoning tasks and the need for larger, more comprehensive datasets.
Frequently Asked Questions
These models were selected based on their performance on multimodal reasoning benchmarks and ability to generate structured thinking tokens. The generated responses were parsed to separate thinking tokens from final answers.
DocVQA offers 50,000 questions on document images, framing document understanding as an extractive question-answering task. Models must produce structured thinking tokens that demonstrate systematic analysis from initial observation to logical conclusion.
The model reveals its step-by-step logical progression instead of mapping inputs directly to outputs. We evaluate a model’s ability to generate coherent, step-by-step reasoning processes when analyzing visual content.
ImageNet-Think captures complete thinking processes from initial observation to final conclusion. ImageNet-21k’s original geographic distribution biases may influence visual content representation.
This limitation hinders reasoning model development and makes diagnosing model failures challenging. This limitation makes it difficult to understand model capabilities and failure modes.
This paper introduces a new dataset called ImageNet-Think-250K, which helps train models that can understand and reason about images and text together. It includes 250,000 images and shows how models think step-by-step before giving answers.