Papers with RM objective

    1 papers
    DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data (2026.acl-long)

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    Challenge: Existing methods for training reward models are vulnerable to context neglect and degraded accuracy.
    Approach: They propose distribution-aware reward modeling that augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response.
    Outcome: The proposed model improves performance in RLHF and improves accuracy in other settings.

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