mwp_ReFT

A deep reinforcement learning-based model fine-tuning framework

CommonProductProgrammingNatural Language ProcessingDeep Learning

ReFT is an open-source research project aimed at fine-tuning large language models using deep reinforcement learning techniques to enhance their performance on specific tasks. The project offers detailed code and data to enable researchers and developers to reproduce the results presented in the papers. The main advantages of ReFT include the ability to automatically adjust model parameters through reinforcement learning and improve model performance on specific tasks via fine-tuning. The product is based on Codellama and Galactica models, adhering to the Apache 2.0 license.

Supports SFT (Supervised Fine-Tuning): Fine-tunes models through supervised learning.
Supports ReFT (Reinforced Fine-Tuning): Fine-tunes models using reinforcement learning techniques.
Supports Online-SL and Offline-SL: Offers both online and offline self-learning options.
Provides checkpoints for various pre-trained models: Allows users to start fine-tuning from different stages.
Supports Top-1 and Voting@100 evaluations: Offers multiple methods for assessing model performance.
Supports Reranking: Enhances model performance through reranking techniques.
Includes detailed experimental guides and scripts: Facilitates quick startups for experiments and fine-tuning.

\The target audience for ReFT consists of researchers and developers in the field of natural language processing
particularly those interested in the application of deep learning and reinforcement learning techniques in language models. This product is well-suited for them as it provides a framework to fine-tune and improve large language models for specific NLP tasks
along with rich experimental data and code support.\

Researchers used ReFT to fine-tune the Codellama model on the GSM8k dataset
improving the model's Top-1 accuracy.
Developers leveraged the ReFT framework to fine-tune the Galactica model on the mathqa dataset
enhancing its Voting@100 performance.
Research teams significantly improved the Rerank@100 performance of their models by applying ReFT and ReFT-Rerank fine-tuning on the svamp dataset.

1. Visit the ReFT GitHub page and clone the code to your local environment.
2. Follow the guidelines in the README.md file to install all dependencies.
3. Select the appropriate pre-trained model and checkpoints to begin fine-tuning.
4. Based on your experimental requirements
run the corresponding shell scripts for model training or evaluation.

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