# RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment

**Authors:** Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian  
**arXiv:** [2307.12950](https://arxiv.org/abs/2307.12950)  
**Date:** 2023-07-24  
**Summary page:** https://www.summarizepaper.com/fr/paper/2307.12950v1/

## Abstract

We propose Reinforcement Learning from Contrast Distillation (RLCD), a method for aligning language models to follow natural language principles without using human feedback. RLCD trains a preference model using simulated preference pairs that contain both a high-quality and low-quality example, generated using contrasting positive and negative prompts. The preference model is then used to improve a base unaligned language model via reinforcement learning. Empirically, RLCD outperforms RLAIF (Bai et al., 2022b) and context distillation (Huang et al., 2022) baselines across three diverse alignment tasks--harmlessness, helpfulness, and story outline generation--and on both 7B and 30B model scales for preference data simulation.