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cs.LG2026
Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning
Xinrui Chen, Jianhao Zhang, Ou Wu +1
Fine-tuning safety aligned large language models (LLMs) on downstream data improves adaptation but may erode learned safety behavior. Existing methods use fixed safety examples, gl…
cs.LG2026
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
Xinrui Chen, Liu Yang, Ou Wu
In Large Language Model (LLM) fine-tuning, parameter and data selection are common strategies for reducing fine-tuning cost, yet they are typically driven by separate scoring mecha…
cs.LG2026
Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion
Ou Wu
Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved samples through selection, aug…