💻 programming

Light-R1

Light-R1 is an open source project focusing on long chain reasoning (Long COT), providing a training method from scratch through curriculum-based SFT, DPO and RL.

#Artificial Intelligence
#Open source
#reinforcement learning
#mathematical model
#long chain reasoning
Light-R1

Product Details

Light-R1 is an open source project developed by Qihoo360 that aims to train long-chain inference models through curriculum-based supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL). This project achieves long-chain reasoning capabilities from scratch through decontaminated data sets and efficient training methods. Its main advantages include open source training data, low-cost training methods, and excellent performance in the field of mathematical reasoning. The project background is based on the current training needs of long-chain inference models and aims to provide a transparent and reproducible training method. The project is currently free and open source, suitable for use by research institutions and developers.

Main Features

1
Provides a long-chain reasoning training method from scratch without relying on pre-trained long-chain reasoning capabilities
2
Open source the complete training data set and code to facilitate researchers to reproduce and improve
3
Use course-based learning to improve model performance through SFT and DPO
4
Support reinforcement learning (RL) training to further optimize model performance
5
Excellent performance in mathematical reasoning, especially on benchmarks such as AIME24 and AIME25

How to Use

1
1. Clone the Light-R1 project code locally.
2
2. Download and install the Python packages that the project depends on.
3
3. Run the SFT training script using the open source training data set.
4
4. Run the DPO training script based on SFT to further optimize the model.
5
5. Use the trained model for inference or continue RL training.

Target Users

The target audience includes artificial intelligence researchers, machine learning engineers, and developers interested in long-chain inference models. This project is suitable for research teams and enterprises who want to train high-performance long-chain inference models with limited resources, and also provides a valuable reference for the open source community.

Examples

Using the Light-R1-7B-DS model achieved an accuracy of 59.1% in the AIME24 test, significantly better than other similar models.

Through curriculum-based SFT and DPO training, Light-R1-32B achieves an accuracy of 76.6% on AIME24, surpassing DeepSeek-R1-Distill-Qwen-32B.

Developers can quickly reproduce the training process of Light-R1 and make customized improvements based on open source training codes and data sets.

Quick Access

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