10 papers
A Heuristic Perspective on Debiasing Language Models
Tian Lan, Yemin Wang, Chuancheng Shi +6
Language models (LMs) often acquire various biases during pre-training and may express them in interactions, potentially causing social harm. Existing methods often rely on counter…
Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry
Weiyang Guo, Zesheng Shi, Longhui Zhang +3
Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effectively leverage failed trajector…
Skill Weaving: Efficient LLM Improvement via Modular Skillpacks
Zhuo Li, Guodong Du, Zesheng Shi +5
Large language models increasingly require specialization across diverse domains, yet existing approaches struggle to balance multi-domain capacities with strict memory and inferen…
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
Wu Li, Yigeng Zhou, Zesheng Shi +3
While recent self-training approaches have reduced reliance on human-labeled data for aligning LLMs, they still face critical limitations: (i) sensitivity to synthetic data quality…
E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning
Weiyang Guo, Zesheng Shi, Liye Zhao +5
While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suf…
Knowledge Fusion of Large Language Models Via Modular SkillPacks
Guodong Du, Zhuo Li, Xuanning Zhou +9
Cross-capability transfer is a key challenge in large language model (LLM) research, with applications in multi-task integration, model compression, and continual learning. Recent…