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Reasoning models

Models trained to work through a problem step by step before answering, trading extra time and tokens at answer time (test-time compute) for better results on hard tasks.

Layer 5 · Foundation Models · Build and run time · Draft, awaiting expert review, reviewed 2026-10-02

Read first: How a model predicts the next token, Post-training: from predictor to assistant.

D1 · StoryFor young children and total beginners

Predict: does the model answer better if it writes its working out first?

Some AI models are taught to think out loud before they answer.

They write rough working, check it and try again, like doing a sum on scrap paper.

Hard questions take them longer, but they get more of them right.

Like: Doing a maths problem on scrap paper before writing the answer.

D2 · PictureFor curious beginners

A reasoning model still predicts one token at a time. The difference is that it first writes a long chain of thought: plans, attempts, checks and corrections, often hidden from the user.

It learns this in post-training, mostly through reinforcement learning on problems with checkable answers such as maths and code. Chains that reach correct answers are rewarded, so useful habits like double-checking get stronger.

Spending more tokens on thinking at answer time is called test-time compute. On hard problems, more thinking tends to help, at the cost of slower and more expensive answers.

How a reasoning model answers

  1. Question: a hard problem
  2. Think: writes a plan
  3. Check: spots a mistake
  4. Answer: after the working

Like: Scrap paper before the final answer.
Where it breaks: A student's working shows how they really solved the problem. A model's written chain of thought is more tokens it predicts, and it doesn't always reflect what actually drove the final answer.

D5 · FrontierFor experts

Open problems: whether the written chain of thought faithfully shows what drove the answer, and how to reward reasoning in areas without checkable answers, such as writing or strategy.

Models can also overthink easy questions, wasting time and money, so deciding how much to think is itself an active research area.

Sources

  1. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)
  2. Learning to reason with LLMs (OpenAI) (2024-09-12)
  3. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025)
  4. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (2024)