Bootstrapping Language Models with DPO Implicit Rewards
Authors: name: Changyu Chen, name: Zichen Liu, name: Chao Du, name: Tianyu Pang, name: Qian Liu, name: Arunesh Sinha, name: Pradeep Varakantham, name: Min Lin

Human alignment in large language models (LLMs) is an active area of
research. A recent groundbreaking work, direct preference optimization (DPO),
has greatly simplified the process from past work in reinforcement learning
from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO,
after training, provides an implicit reward model. In this work, we make a
novel observation that this implicit reward model can by itself be used in a
bootstrapping fashion to further align the LLM. Our approach is to use the
rewards from a current LLM model to construct a preference dataset, which is
then used in subsequent DPO rounds. We incorporate refinements that debias the
length of the responses and improve the quality of the preference dataset to
further improve our approach. Our approach, named self-alignment with DPO
ImpliCit rEwards (DICE), shows great improvements in alignment and achieves
superior performance than Gemini Pro on AlpacaEval 2, reaching 27.55%
length-controlled win rate against GPT-4 Turbo, but with only 8B parameters and
no external feedback. Our code is available at https://github.com/sail-sg/dice.