1.5k citations · 1.6k across the 30 of their papers we have counts for
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Enhancing LLM Reasoning with Reward-guided Tree Search
Jinhao Jiang, Zhipeng Chen, Yingqian Min +12
Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By alloc…
YuLan-Mini: An Open Data-efficient Language Model
Yiwen Hu, Huatong Song, Jia Deng +8
Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper p…
Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
Yingqian Min, Zhipeng Chen, Jinhao Jiang +11
Recently, slow-thinking reasoning systems, such as o1, have demonstrated remarkable capabilities in solving complex reasoning tasks. These systems typically engage in an extended t…
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Jinhao Jiang, Jiayi Chen, Junyi Li +5
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and t…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
Jinhao Jiang, Junyi Li, Wayne Xin Zhao +3
Adapting general large language models (LLMs) to specialized domains presents great challenges due to varied data distributions. This adaptation typically requires continual pre-tr…
KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph
Jinhao Jiang, Kun Zhou, Wayne Xin Zhao +4
In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that de…