Self-Exploring Language Models: Active Preference Elicitation for Online
  Alignment
Authors: name: Shenao Zhang, name: Donghan Yu, name: Hiteshi Sharma, name: Ziyi Yang, name: Shuohang Wang, name: Hany Hassan, name: Zhaoran Wang

Preference optimization, particularly through Reinforcement Learning from
Human Feedback (RLHF), has achieved significant success in aligning Large
Language Models (LLMs) to adhere to human intentions. Unlike offline alignment
with a fixed dataset, online feedback collection from humans or AI on model
generations typically leads to more capable reward models and better-aligned
LLMs through an iterative process. However, achieving a globally accurate
reward model requires systematic exploration to generate diverse responses that
span the vast space of natural language. Random sampling from standard
reward-maximizing LLMs alone is insufficient to fulfill this requirement. To
address this issue, we propose a bilevel objective optimistically biased
towards potentially high-reward responses to actively explore
out-of-distribution regions. By solving the inner-level problem with the
reparameterized reward function, the resulting algorithm, named Self-Exploring
Language Models (SELM), eliminates the need for a separate RM and iteratively
updates the LLM with a straightforward objective. Compared to Direct Preference
Optimization (DPO), the SELM objective reduces indiscriminate favor of unseen
extrapolations and enhances exploration efficiency. Our experimental results
demonstrate that when finetuned on Zephyr-7B-SFT and Llama-3-8B-Instruct
models, SELM significantly boosts the performance on instruction-following
benchmarks such as MT-Bench and AlpacaEval 2.0, as well as various standard
academic benchmarks in different settings. Our code and models are available at
https://github.com/shenao-zhang/SELM.