BOND: Aligning LLMs with Best-of-N Distillation
Pier Giuseppe Sessa Robert Dadashi-Tazehozi Leonard Hussenot Johan Ferret Nino Vieillard Alexandre Rame Bobak Shahriari Sarah Perrin Abram L. Friesen Geoffrey Cideron Sertan Girgin Piotr Stanczyk Andrea Michi Danila Sinopalnikov Sabela Ramos Garea Amélie Héliou Aliaksei Severyn Matthew Hoffman Nikola Momchev Olivier Bachem
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models.
Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates.
In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We use the Jeffreys divergence (a linear combination of forward and backward KL) to balance between mode-covering and mode-seeking behavior, and derive an iterative formulation that utilizes a moving anchor for efficiency. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models.
LLM, Alignment, RLHF, Best-of-N
We propose a novel RLHF approach to align LLMs via online distillation of Best-of-N sampling policies.