思维链 vs 思维树:该用哪个

2026-08-01 约 5 分钟阅读

CoT和ToT是两大推理技术,但适用不同问题。

Chain of Thought vs Tree of Thought: Which Reasoning Technique Should You Use in 2026

Chain of Thought (Wei et al., 2022) and Tree of Thought (Yao et al., 2023) both make LLMs reason more carefully - but they are built for different kinds of problems. Understanding the difference saves you tokens and improves accuracy.

The Core Difference

Chain of Thought (CoT): The model produces one linear chain of reasoning, then answers. One path, sequential.

Step 1 -> Step 2 -> Step 3 -> Answer

Tree of Thought (ToT): The model generates multiple reasoning branches at each step, evaluates them, and explores the most promising. Multiple paths, with backtracking.

When to Use Which

Problem type Technique Why
Math, arithmetic CoT Single correct path, linear reasoning
Logic puzzles CoT Step-by-step suffices
Open-ended planning ToT Explore many options
Creative writing ToT Branch into different directions
Debugging CoT Trace one hypothesis at a time
Strategy/games ToT Evaluate moves before committing
Short answers CoT Cheap and effective
Complex reasoning with dead ends ToT Backtracking avoids traps

Cost Comparison

Technique Tokens Latency Best model size
Direct answer 1x Fast Any
CoT 3-5x Medium 7B+
ToT 10-30x Slow 30B+ or API

How to Implement ToT Without a Framework

A minimal loop: ask the model for 3 candidate next steps, score each with a second prompt ("rate this step 1-10"), keep the best, repeat 3-5 times. That is ToT in plain Python - no framework needed.

FAQ

Is ToT always better? No. For well-defined problems with a single correct path, CoT is cheaper and equally accurate. ToT shines when branches exist and dead ends are common.

Do modern models still need these techniques? Yes - even GPT-5-class models improve on hard tasks with explicit reasoning scaffolding, though newer models do more internal reasoning natively.

Which should a beginner learn first? CoT. It is simpler, cheaper, and covers 80% of practical cases.

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