1 citations · 1 across the 10 of their papers we have counts for
10 papers
Towards Agentic Self-Learning LLMs in Search Environment
Wangtao Sun, Xiang Cheng, Jialin Fan +5
We study whether self-learning can scale LLM-based agents without relying on human-curated datasets or predefined rule-based rewards. Through controlled experiments in a search-age…
Amplify Adjacent Token Differences: Enhancing Long Chain-of-Thought Reasoning with Shift-FFN
Yao Xu, Mingyu Xu, Fangyu Lei +7
Recently, models such as OpenAI-o1 and DeepSeek-R1 have demonstrated remarkable performance on complex reasoning tasks through Long Chain-of-Thought (Long-CoT) reasoning. Although…
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate
Ziyang Huang, Wangtao Sun, Jun Zhao +1
This paper systematically addresses the challenges of rule retrieval, a crucial yet underexplored area. Vanilla retrieval methods using sparse or dense retrievers to directly searc…
Probabilistic Uncertain Reward Model
Wangtao Sun, Xiang Cheng, Xing Yu +5
Reinforcement learning from human feedback (RLHF) is a critical technique for training large language models. However, conventional reward models based on the Bradley-Terry model (…
Shuttle Between the Instructions and the Parameters of Large Language Models
Wangtao Sun, Haotian Xu, Huanxuan Liao +5
The interaction with Large Language Models (LLMs) through instructions has been extensively investigated in the research community. While instructions have been widely used as the…
ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model
Xuanqing Yu, Wangtao Sun, Jingwei Li +3
In the realm of event prediction, temporal knowledge graph forecasting (TKGF) stands as a pivotal technique. Previous approaches face the challenges of not utilizing experience dur…
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
Wangtao Sun, Chenxiang Zhang, XueYou Zhang +7
Although Large Language Models (LLMs) have demonstrated strong ability, they are further supposed to be controlled and guided by in real-world scenarios to be safe, accurate, and i…
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Wangtao Sun, Haotian Xu, Xuanqing Yu +4
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited a…
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
Wangtao Sun, Shizhu He, Jun Zhao +1
With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies…
ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook
Wangtao Sun, Xuanqing Yu, Shizhu He +2
Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers. However, LLMs still fail in many specific tasks a…