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cs.LG2026
Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions
Wenhao Zhang, Lin Mu, Li Ni +2
Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models (LLMs), but its strictly linear structure fundamentally limits expressive ca…
cs.AI2026
IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning
Haohao Luo, Zexi Li, Yuexiang Xie +3
Deep Research (DR) agents extend Large Language Models (LLMs) beyond parametric knowledge by autonomously retrieving and synthesizing evidence from large web corpora into long-form…
cs.LG2026
On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
Shumin Wang, Yuexiang Xie, Wenhao Zhang +4
Entropy serves as a critical metric for measuring the diversity of outputs generated by large language models (LLMs), providing valuable insights into their exploration capabilitie…