---
title: "S1: Achieving O1-Level Reasoning with Just $50"
date: 2025-02-08T10:00:00.000Z
author: Z.SHINCHVEN
tags: [AI, Machine Learning, Reasoning Models, OpenAI, Test-Time Scaling]
canonical: https://atlassc.net/2025/02/08/s1-simple-test-time-scaling
---
## Li Fei-Fei's Team Trains an O1-Level Reasoning Model for Just $50!

The AI community has been buzzing about OpenAI's **O1 model**, which demonstrated **remarkable test-time scaling** and strong reasoning capabilities. However, the methodology behind O1 has remained undisclosed—until now. **A team led by Li Fei-Fei at Stanford** has introduced **S1**, a reasoning model that not only replicates but in some cases surpasses O1's performance—all with a mere **$50 training cost**.

## What Is Test-Time Scaling, and Why Does It Matter?

Traditional AI models improve by increasing training compute (e.g., GPT scaling laws). However, a new paradigm—**test-time scaling**—enhances model performance by allocating **more compute during inference**. Instead of massive datasets and expensive training, **test-time compute is used strategically to improve answers dynamically**. OpenAI's O1 hinted at this potential but didn't reveal how to achieve it. Enter **S1: a radically simple and open approach to test-time scaling**.

## Key Innovations in S1

S1 achieves its impressive reasoning capabilities through a few key innovations. Instead of relying on massive datasets, S1 was fine-tuned on just **1,000 carefully curated reasoning samples (s1K)**. Another significant breakthrough is **budget forcing**, a technique that controls the time a model spends reasoning, leading to improved accuracy with controlled compute costs. Despite utilizing minimal resources, **S1-32B outperforms O1-preview on competition-level math problems**, including AIME24 and MATH500.

## How Did They Do It?

The foundation of S1’s success lies in its **curated dataset (s1K)**, which prioritizes quality over quantity. The research team selected challenging and diverse questions from sources like AIME, Olympiad competitions, and PhD-level problems, while also distilling reasoning traces from Google Gemini to strengthen its training base. Instead of training a model from scratch, they fine-tuned **Qwen2.5-32B-Instruct**, a publicly available model, completing the process in just **26 minutes on 16 NVIDIA H100 GPUs**, costing only **$50 worth of compute**.

One of the most intriguing aspects of S1 is its **budget forcing mechanism**. By modulating how long the model spends reasoning, S1 can effectively double-check its own thought process. If the model attempts to terminate its reasoning too soon, it is prompted with a simple “Wait” command, often leading to self-correction and more accurate answers.

## S1 vs. OpenAI's O1: The Showdown

| Model | AIME24 (Math) | MATH500 | GPQA (PhD-Level Science) |
|--------|---------|---------|---------|
| **S1-32B** | **56.7%** | **93.0%** | **59.6%** |
| O1-preview | 44.6% | 85.5% | 73.3% |
| DeepSeek R1 | 79.8% | 97.3% | 71.5% |

**Takeaway:** S1 **beats OpenAI’s O1-preview** on competition math, while using only **1,000 samples and simple test-time tricks**. It’s **the most sample-efficient open reasoning model**.

## Why This Matters

S1 is significant for several reasons. It is the **first fully open alternative to OpenAI’s O1**, providing transparent access to its **code, data, and model weights**. Additionally, it demonstrates that **strong AI models no longer require multimillion-dollar compute resources**—S1 achieves comparable performance at just **$50**. More broadly, test-time scaling challenges the notion that larger training datasets always lead to better models, proving that **smarter inference techniques can be just as powerful**.

## Where Can You Try S1?

You can find the full **model, dataset, and code** on GitHub:

➡️ **[S1 GitHub Repository](https://github.com/simplescaling/s1)**

The model is also available on Hugging Face:

➡️ **[S1 on Hugging Face](https://huggingface.co/simplescaling/s1-32B)**

For more details on the research behind S1, you can read the full paper:

➡️ **[S1 Paper on arXiv](https://arxiv.org/abs/2501.19393)**

With test-time scaling and budget forcing, **S1 proves that strong reasoning doesn’t have to come with a massive price tag**. This work could redefine how we build and scale **next-generation AI models**.

What do you think? Is test-time scaling the future of AI? Let’s discuss in the comments!