  Direct alignment from preferences (DAP) has emerged as a promising paradigm
for aligning large language models (LLMs) to human desiderata from
pre-collected, offline preference datasets. While recent studies indicate that
existing offline DAP methods can directly benefit from online training samples,
we highlight the need to develop specific online DAP algorithms to fully
harness the power of online training. Specifically, we identify that the
learned LLM should adhere to the proximity of the behavior LLM, which collects
the training samples. To this end, we propose online Preference Optimization in
proximity to the Behavior LLM (BPO), emphasizing the importance of constructing
a proper trust region for LLM alignment.
  We conduct extensive experiments to validate the effectiveness and
applicability of our approach by integrating it with various DAP methods,
resulting in significant performance improvements across a wide range of tasks
when training with the same amount of preference data. Even when only
introducing one additional data collection phase, our online BPO improves its
offline DAP baseline from 72.0% to 80.2% on TL;DR and from 82.2% to 89.1% on
Anthropic Helpfulness in terms of win rate against human reference text.
