  Effectively aligning Large Language Models (LLMs) with human-centric values
while preventing the degradation of abilities acquired through Pre-training and
Supervised Fine-tuning (SFT) poses a central challenge in Reinforcement
Learning from Human Feedback (RLHF). In this paper, we first discover that
interpolating RLHF and SFT model parameters can adjust the trade-off between
human preference and basic capabilities, thereby reducing the alignment tax at
the cost of alignment reward. Inspired by this, we propose integrating the RL
policy and SFT models at each optimization step in RLHF to continuously
regulate the training direction, introducing the Online Merging Optimizer.
Specifically, we merge gradients with the parameter differences between SFT and
pretrained models, effectively steering the gradient towards maximizing rewards
in the direction of SFT optimization. We demonstrate that our optimizer works
well with different LLM families, such as Qwen and LLaMA, across various model
sizes ranging from 1.8B to 8B, various RLHF algorithms like DPO and KTO, and
existing model merging methods. It significantly enhances alignment reward
while mitigating alignment tax, achieving higher overall performance across 14
benchmarks.
