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Pre-DPO: Improving Language Model Performance with a Guiding Reference

Researchers have unveiled “Pre-DPO,” a novel training method that significantly enhances the performance of large language models (LLMs) by improving how they learn from human feedback. The technique, detailed in a recent paper, builds upon existing methods like Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO) without requiring additional data or external models.

The core idea behind Pre-DPO lies in strategically using a “guiding reference model” to direct the LLM’s learning process. Think of it like having an experienced tutor who knows which examples will be most helpful for a student to understand a difficult concept.

Traditional DPO initializes both the policy model (the main LLM being trained) and a reference model (used to regulate the training process) identically. The researchers found this approach can lead to inefficient data utilization because the reference model tends to constrain the policy too early in training, creating a performance ceiling. SimPO, on the other hand, avoids a reference model altogether, which makes it efficient but can be more susceptible to catastrophic forgetting – losing previously learned abilities.

Pre-DPO tackles these challenges by first optimizing the policy model using either DPO or SimPO. The resulting optimized model then becomes the “guiding reference model.” Subsequently, the original policy model is re-optimized, but this time, guided by the insights of the “experienced” reference model.

The authors liken the role of the guiding reference model to a data weight adjuster. The model adaptively assigns higher weights to training examples that are more suitable for the policy model and lower weights to those that are less suitable. For example, consider a language model learning to generate creative stories. Some sentence structures or plot twists may be easier for the model to grasp initially. The guiding reference model helps emphasize these easier examples early on, allowing the model to build a strong foundation before tackling more complex narrative elements.

The paper presents experimental results using two prominent LLM series, Llama3.2 and Qwen2.5. The results show that Pre-DPO consistently improves the performance of both DPO and SimPO on benchmark datasets such as AlpacaEval 2.0 and Arena-Hard v0.1. In essence, Pre-DPO pushes LLMs to better results compared to vanilla DPO or SimPO.

For example, the method achieves average improvements of 2.5 points in the AlpacaEval 2 length-controlled win rate and 2.6 points in the Arena-Hard v0.1 win rate. “By introducing the guiding reference model, Pre-DPO can further improve the performance of existing well-tuned preference optimization methods,” the authors explain.

The authors highlight that Pre-DPO doesn’t rely on external models or additional data, making it both flexible and easy to deploy. Given the improvements, this technique is likely to become part of the toolkit for language model training. The code and data for the study are publicly available.