# Reinforced Self-Training (ReST) for Language Modeling

**Authors:** Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas  
**arXiv:** [2308.08998](https://arxiv.org/abs/2308.08998)  
**Date:** 2023-08-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.08998v2/

## Key points

- The paper introduces a method called Reinforced Self-Training (ReST) for improving the quality of large language models (LLMs)
- ReST aligns LLM outputs with human preferences
- The algorithm generates a dataset by sampling from an initial LLM policy
- The dataset is used to train the LLM policy using offline RL algorithms
- ReST offers greater efficiency compared to typical online RLHF methods because the training dataset is produced offline, allowing for data reuse
- The focus of the paper is on applying ReST to machine translation
- Results show that ReST significantly enhances translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks
- Authors provide additional context about the paper, including authors' names and affiliations, categories of computer science in computational linguistics (cs.CL) and machine learning (cs.LG), page count, figures included, publication date, and last update date
- In summary, ReST is a novel approach for improving language models' outputs through reinforcement learning from human feedback. It demonstrates significant improvements in translation quality while being computationally efficient and sample-efficient.

## Summary

The paper titled "Reinforced Self-Training (ReST) for Language Modeling" introduces a method called Reinforced Self-Training (ReST) that aims to improve the quality of large language models (LLMs) by aligning their outputs with human preferences. The authors propose a simple algorithm inspired by growing batch reinforcement learning (RL), which generates a dataset by sampling from an initial LLM policy. This dataset is then used to train the LLM policy using offline RL algorithms. Compared to typical online RLHF methods, ReST offers greater efficiency because the training dataset is produced offline, allowing for data reuse. Although ReST is applicable to all generative learning settings, the focus of this paper is on its application to machine translation. The results demonstrate that ReST significantly enhances translation quality, as measured by both automated metrics and human evaluation on machine translation benchmarks. The authors also provide some additional context about the paper. It was authored by Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat and Nando de Freitas. The paper falls under the categories of computer science in computational linguistics (cs.CL) and machine learning (cs.LG). It consists of 23 pages and includes 16 figures. The paper was published on August 17th 2023 and last updated on August 21st 2023. In summary, this paper presents a novel approach called ReST for improving language models' outputs through reinforcement learning from human feedback. The proposed method demonstrates significant improvements in translation quality while being computationally efficient and sample-efficient.