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cs.LG2026
Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization
Hao Wang, Kun Yuan, Wenlin Zhong +4
Open-weight language models from different families exhibit complementary capabilities, motivating their consolidation into a compact student through on-policy distillation (OPD).…
cs.LG2026
Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
Hao Wang, Guozhi Wang, Han Xiao +8
Reinforcement learning (RL) has been widely used to train LLM agents for multi-turn interactive tasks, but its sample efficiency is severely limited by sparse rewards and long hori…
cs.LG2026
FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models
Juyong Jiang, Fan Wang, Hong Qi +2
Parameter-efficient fine-tuning (PEFT) has emerged as a crucial paradigm for adapting large language models (LLMs) under constrained computational budgets. However, standard PEFT m…