How to Intermediate · 3 min read

How reasoning models are trained

Quick answer

Reasoning models are trained by combining supervised learning on reasoning-specific datasets, reinforcement learning from human feedback (RLHF), and techniques like chain-of-thought prompting to improve step-by-step logical inference. These methods teach models to generate coherent, multi-step reasoning rather than just pattern matching.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python package and set your API key as an environment variable to access reasoning-capable models like gpt-4o or claude-3-5-sonnet-20241022.

bash

pip install openai

Step by step training overview

Training reasoning models involves three main stages:

Supervised fine-tuning: Models are trained on datasets with explicit reasoning steps, such as math proofs or logic puzzles, to learn multi-step inference.
Reinforcement learning from human feedback (RLHF): Human evaluators rank model outputs, guiding the model to prefer clearer, more accurate reasoning.
Chain-of-thought prompting: During inference, models are prompted to generate intermediate reasoning steps, improving final answer accuracy.

This combination enables models to perform complex reasoning tasks beyond simple pattern recognition.

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Example: prompt with chain-of-thought to improve reasoning
messages = [
    {"role": "user", "content": "Q: If all bloops are razzies and all razzies are lazzies, are all bloops definitely lazzies? Explain step-by-step."}
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages
)

print(response.choices[0].message.content)

output

Yes, all bloops are definitely lazzies. Step 1: All bloops are razzies. Step 2: All razzies are lazzies. Step 3: Therefore, all bloops are lazzies by transitive property.

Common variations

You can train reasoning models using different approaches:

Asynchronous training: Use distributed training frameworks to scale fine-tuning on large reasoning datasets.
Different models: Use claude-3-5-sonnet-20241022 for strong reasoning or mistral-large-latest for cost-effective alternatives.
Prompt engineering: Experiment with few-shot chain-of-thought examples to boost reasoning without retraining.

Troubleshooting

If your model outputs shallow or incorrect reasoning:

Ensure training data includes detailed reasoning steps, not just final answers.
Use RLHF to align model outputs with human logical standards.
Try chain-of-thought prompting to guide the model during inference.
Check for token limits that might truncate reasoning chains.

✅

Key Takeaways

Train reasoning models with supervised datasets containing explicit multi-step logic.
Use reinforcement learning from human feedback to improve reasoning quality.
Chain-of-thought prompting enhances reasoning during inference without retraining.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022, mistral-large-latest

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