7 citations · 19 across the 23 of their papers we have counts for
4 papers · 1 filter
D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
Bowen Xu, Shaoyu Wu, Hao Jiang +4
Effective tool use and reasoning are essential capabilities for large reasoning models~(LRMs) to address complex real-world problems. Through empirical analysis, we identify that c…
From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs
Haonan Wang, Weida Liang, Zihang Fu +8
Recent reasoning LLMs (RLMs), especially those trained with verifier-based reinforcement learning, often perform worse with few-shot CoT than with direct answering. We revisit this…
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
Tianwei Lin, Jiang Liu, Wenqiao Zhang +9
While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially…
Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation
Yige Shen, Hao Jiang, Hua Qu +1
Despite their impressive capabilities, large language models (LLMs) often face challenges such as temporal misalignment and generating hallucinatory content. Enhancing LLMs with re…