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This paper provides the first systematic analysis of error sources in trajectory-based data attribution methods, identifies optimizer mismatch as the dominant error, proposes AdamW-influence to address it, and offers practical guidelines for data selection via a K-step look-ahead framework.
Research paper investigating performance degradation when using the Muon optimizer instead of Adam for fine-tuning pretrained models, demonstrating that parameter-efficient methods like LoRA effectively mitigate this optimizer mismatch across language and vision tasks.