Selective Preference Optimization via Token-Level Reward Function Estimation
Keywords: large language models, preference optimization, alignment
TL;DR: We propose Selective Preference Optimization (SePO), a novel selective alignment strategy that centers on efficient key token selection.
Abstract: Recent advancements in large language model alignment leverage token-level supervisions to perform fine-grained preference optimization. However, existing token-level alignment methods either optimize on all available tokens, which can be noisy and inefficient, or perform selective training with complex and expensive key token selection strategies.
In this work, we propose Selective Preference Optimization (SePO), a novel selective alignment strategy that centers on efficient key token selection without requiring strong, fine-grained supervision signals. We theoretically prove the feasibility of Direct Preference Optimization (DPO) as token-level reward function estimators, which applies to any existing alignment datasets and enables cost-efficient token selection with small-scale model sizes and training data. We then train an oracle model with DPO on the target data and utilize the estimated reward function to score all tokens within the target dataset, where only the key tokens are selected to supervise the target policy model with a contrastive objective function. Extensive experiments on three public evaluation benchmarks show that SePO significantly outperforms competitive baseline methods by only optimizing on 30\% key tokens. We also explore SePO as a new paradigm for weak-to-strong generalization, showing
that weak oracle models effectively supervise strong policy models with up to 16.8$\times$ more parameters. SePO also selects useful supervision signals from out-of-distribution data, alleviating the over-optimization problem.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 6269
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