Papers by Peijia Zheng

    1 papers
    Intrinsic Mutual Information as a Modulator for Preference Optimization (2026.findings-acl)

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    Challenge: Existing methods for offline preference optimization involve additional hyperparameter tuning, resulting in substantial time overhead.
    Approach: They propose a lightweight framework for offline preference optimization that leverages hyperparameter modulation to decouple preference contributions.
    Outcome: The proposed framework achieves superior performance over existing methods while reducing training overhead by more than 15%.

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