# simplescaling/s1

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6,656 stars · 757 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/simplescaling/s1
- Homepage: https://arxiv.org/abs/2501.19393
- awesome-repositories: https://awesome-repositories.com/repository/simplescaling-s1.md

## Description

s1 is a reasoning training framework and GPU cluster orchestrator designed to build and refine large language models. It provides a system for executing supervised fine-tuning on distributed hardware, utilizing gradient checkpointing and hardware optimization to improve model reasoning.

The project features a synthetic data generator and dataset builder that produce high-quality training sets. This workflow collects questions, generates model reasoning traces, and applies automated grading loops to filter for correct answers.

The framework includes an evaluation suite to compute accuracy and statistical metrics on standardized benchmarks. It also implements test-time scaling techniques to increase reasoning accuracy by expanding the computational search space during the inference phase.

## Tags

### Artificial Intelligence & ML

- [Distributed Training Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-orchestration.md) — Orchestrates the distribution of large-scale model training tasks across multiple GPU hardware nodes.
- [Automated Evaluation Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/evaluation-metrics/scoring-pipelines/automated-evaluation-loops.md) — Implements automated loops that generate model reasoning traces and score them for inclusion in training sets.
- [Model Performance Benchmarking](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/model-analysis/model-performance-benchmarking.md) — Computes accuracy and token statistics by comparing model samples against standardized reasoning evaluation sets.
- [Reasoning Model Training Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/reasoning-model-training-suites.md) — Integrates dataset building, supervised fine-tuning, and test-time scaling to train models for complex reasoning.
- [Supervised Fine-Tuning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-fine-tuning-frameworks.md) — Provides a pipeline for supervised fine-tuning to optimize model reasoning using gradient checkpointing and hardware acceleration.
- [Synthetic Reasoning Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/synthetic-reasoning-data-generators.md) — Provides pipelines for creating high-quality mathematical and logical reasoning datasets through trace generation and grading. ([source](https://github.com/simplescaling/s1))
- [Reasoning Evaluations](https://awesome-repositories.com/f/artificial-intelligence-ml/long-context-training-optimizations/long-context-retrieval-testing/reasoning-evaluations.md) — Analyzes sample outputs and token usage across benchmarks to evaluate a model's reasoning and comprehension abilities. ([source](https://github.com/simplescaling/s1))

### Part of an Awesome List

- [Reasoning Dataset Builders](https://awesome-repositories.com/f/awesome-lists/ai/reasoning-datasets/reasoning-dataset-builders.md) — Implements a workflow for collecting questions and generating filtered model traces to create training sets.
- [Reasoning Trace Generators](https://awesome-repositories.com/f/awesome-lists/data/dataset-curation-and-generation/reasoning-trace-generators.md) — Produces training data by collecting reasoning paths and filtering them through automated grading passes.
- [Reasoning Models](https://awesome-repositories.com/f/awesome-lists/ai/reasoning-models.md) — Simplified scaling laws for reasoning model training.

### DevOps & Infrastructure

- [GPU Training Clusters](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure-management/gpu-training-clusters.md) — Manages GPU resources and executes supervised fine-tuning with hardware optimization and gradient checkpointing.

### Testing & Quality Assurance

- [Model Evaluation Benchmarks](https://awesome-repositories.com/f/testing-quality-assurance/model-evaluation-benchmarks.md) — Measures logical performance and token efficiency using standardized benchmarks to determine reasoning accuracy.
- [LLM Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/llm-evaluation.md) — Computes accuracy and statistical metrics on reasoning tasks using automated judges and standardized benchmarks.

### Data & Databases

- [Test-Time Compute Scalers](https://awesome-repositories.com/f/data-databases/horizontal-database-scaling/trace-storage-scaling/stateless-compute-scaling/test-time-compute-scalers.md) — Increases reasoning accuracy by expanding the computational search space and sampling during the inference phase.