8 citations · 14 across the 20 of their papers we have counts for
6 papers · 1 filter
TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
Shenzhi Yang, Guangcheng Zhu, Xing Zheng +7
Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimiz…
Merge-of-Thought Distillation
Zhanming Shen, Zeyu Qin, Zenan Huang +6
Efficient reasoning distillation for long chain-of-thought (CoT) models is increasingly constrained by the assumption of a single oracle teacher, despite the practical availability…
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
Zhanming Shen, Hao Chen, Yulei Tang +6
Instruction tuning is vital for aligning large language models (LLMs) with human intent, but current methods typically rely on costly human-annotated seed data or powerful external…
SPA++: Generalized Graph Spectral Alignment for Versatile Domain Adaptation
Zhiqing Xiao, Haobo Wang, Xu Lu +3
Domain Adaptation (DA) aims to transfer knowledge from a labeled source domain to an unlabeled or sparsely labeled target domain under domain shifts. Most prior works focus on capt…
Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
Jiaming Tian, Liyao Li, Wentao Ye +6
Tables are fundamental in domains such as finance, healthcare, and public administration, yet real-world table tasks often involve noise, structural heterogeneity, and semantic com…
Towards Robust Incremental Learning under Ambiguous Supervision
Rui Wang, Mingxuan Xia, Chang Yao +4
Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotat…