Chain-of-Thought Reasoning

Chain-of-thought (CoT) reasoning is a technique that enables vision-language models (VLMs) to solve complex visual tasks by breaking them down into explicit, step-by-step reasoning processes. Datature Vi supports chain-of-thought reasoning through system prompt configuration and training data with annotated reasoning steps. Instead of generating a direct answer, the model articulates its reasoning path, leading to more accurate and interpretable results.

This is similar to showing your work in a math problem. Rather than jumping straight to an answer, the model explains its reasoning: "I see three red boxes on the left shelf, two blue boxes on the right shelf, so there are five boxes total."

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Best for

By the end of this guide

Understand how chain-of-thought reasoning improves accuracy on complex visual tasks.

How chain-of-thought reasoning works

Accuracy Improvement

Chain-of-thought reasoning can improve accuracy on complex VQA tasks by reducing the chance of the model skipping intermediate steps, while also providing transparent explanations of how the model reached its conclusions.

Common use cases

Chain-of-thought vs. direct reasoning

Question type

Direct reasoning

CoT reasoning

When to use CoT

Tips for better chain-of-thought results

Tip

Good example

Poor example

Include reasoning examples in training data. When creating VQA annotations, include step-by-step answers using <datature_think> tags. This teaches the model to reason before answering. See Annotation Guide for the format.

Validate intermediate steps, not just the final answer. Check that observations are visually accurate, that each step follows logically, and that the conclusion matches the reasoning. This distinguishes perception errors (seeing wrong) from logic errors. See Configure Your System Prompt for prompt design guidelines.

How to enable CoT in Datature Vi

Chain-of-thought reasoning can be enabled through annotation format, through training, and at inference time by passing cot=True on ViModel(...) (see Run Inference).

Annotation-side

To train a model with CoT reasoning, include reasoning steps in your annotations using <datature_think> tags. During training, Datature Vi converts these to the model's native <think> tags.

Inference-side

Pass cot=True on ViModel(...) (same keyword the underlying predictor accepts). That enables CoT decoding at inference time: the model outputs <think> and <answer> tags, and the Vi SDK parses them into thinking and the task-specific result fields.

Frequently asked questions

For a deep dive into CoT for VLMs, see the Introduction to Chain-of-Thought for Vision-Language Models blog post and the CoT glossary entry.