DynamicFocalPO: Adaptive Focusing Strategy for Preference Optimization

Recent preference optimization algorithms such as Direct Preference Optimization (DPO) have become prevalent for aligning large language models (LLMs) with human preferences. FocalPO improves upon DPO by introducing a modulating factor that down-weighs misranked preference pairs. However, using a fixed modulating factor throughout training is suboptimal, as the model's learning capacity evolves during training. We introduce DynamicFocalPO, which employs a dynamic focusing strategy that adapts over the course of training. Inspired by curriculum learning, our method initially focuses on correctly ranked samples to establish a solid foundation, then gradually incorporates harder samples as training progresses. Experiments demonstrate that DynamicFocalPO surpasses both DPO and FocalPO on benchmarks including Alpaca Eval 2.0 and Arena-Hard using Mistral-Base-7B and Llama-3-Instruct-8B. We further provide theoretical analysis showing that the dynamic schedule enables adaptive entropy regularization and selective gradient suppression.