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cs.CL2026
Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
Cunchun Li, Haonan He, Yifan Gao +4
Supervised fine-tuning (SFT) learns most aggressively from tokens that the model deems least likely. This helps acquire new behaviors, but also amplifies noisy or conflicting super…
cs.CL2026
MoE-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation
Qingyu Yang, Haonan He, Minglei Li +4
Mixture-of-Experts (MoE) architectures have been widely adopted in large language models, yet parameter-efficient fine-tuning (PEFT) for MoE models remains underexplored. Existing…
cs.CL2026
Parametric Skills
Xuan Zhao, Haonan He, Qingyu Yang +5
Since intelligence fundamentally relies on efficient skill acquisition (Chollet, 2019), the ability to leverage skills is critical. For LLMs, skills, manually authored or extracted…