Modulated Intervention Preference Optimization (MIPO): Keep the Easy, Refine the Difficult

ACL ARR 2025 February Submission1645 Authors

Abstract: Preference optimization methods typically begin training with a well-trained SFT model as a reference model. In RLHF and DPO, a regularization term is used during the preference optimization process to prevent the policy model from deviating too far from the reference model's distribution, thereby avoiding the generation of anomalous responses. However, if the reference model is not aligned with the given data and requires significant deviation from its current state, a regularization term may actually hinder the model alignment. In this study, we propose \textbf{Modulated Intervention Preference Optimization (MIPO)} to address this issue. MIPO modulates the degree of intervention from the reference model based on how well the given data is aligned with it. If the data is well-aligned, the intervention is increased to prevent the policy model from diverging significantly from reference model. Conversely, if the alignment is poor, the interference is reduced to facilitate more extensive training. We compare MIPO and DPO using Mistral-7B and Llama3-8B on Alpaca Eval 2.0 and MT-Bench, showing that MIPO consistently outperforms DPO across various evaluation scenarios

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: optimization methods

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 1645