RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment

Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian

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Keywords: Language Model, RLHF, Alignment, Instruction Tuning

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TL;DR: We propose a new method for simulating preference data in RLHF alignment pipelines based on generating preference pairs from two contrasting prompts, with strong downstream performance on three diverse alignment tasks and multiple LLaMA model scales.

Abstract: We propose Reinforcement Learning from Contrastive Distillation (RLCD), a method for aligning language models to follow principles expressed in natural language (e.g., to be more harmless) without using human feedback. RLCD creates preference pairs from two contrasting model outputs, one using a positive prompt designed to encourage following the given principles, and one using a negative prompt designed to encourage violating them. Using two different prompts causes model outputs to be more differentiated on average, resulting in cleaner preference labels in the absence of human annotations. We then use the preference pairs to train a preference model, which is in turn 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 when using both 7B and 30B model scales for simulating preference data

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Primary Area: generative models

Submission Number: 4026