9 citations · 14 across the 22 of their papers we have counts for
13 papers · 1 filter
DataFoundry: Evolving Data Preparators via Recursive Self-Improvement
Cehao Yang, Xiaojun Wu, Xueyuan Lin +4
Domain adaptation of large language models increasingly depends on constructing high-quality training data, yet existing data-preparation pipelines typically address quality only a…
Unsupervised Post-Training of Foundation Models: A Survey
Yijie Xu, Qianyi Cai, Huizai Yao +9
Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-Training (UPT): update-bearin…
Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL
Xiaojun Wu, Cehao Yang, Honghao Liu +7
Reinforcement learning (RL) for terminal agents needs executable training environments with reliable rewards and useful difficulty. Fixed recipes such as few-shot, Self-Instruct, a…
LazyTrain: Limited-resource Allocation toward Zero-waste Yield Optimization in Large Language Model Training
Xiaojun Wu, Cehao Yang, Honghao Liu +5
Training large language models on limited hardware is increasingly a scheduling problem across GPU compute, host memory, PCIe transfer, and storage bandwidth. Existing offloading s…
Bayesian-Agent: Posterior-Guided Skill Evolution Across LLM Agent Harnesses
Xiaojun Wu, Cehao Yang, Honghao Liu +7
LLM agents increasingly rely on prompts, tools, memory, SOPs, skills, and harness feedback, yet current self-evolution pipelines often update these assets through heuristic reflect…
RETuning: Upgrading Inference-Time Scaling for Stock Movement Prediction with Large Language Models
Xueyuan Lin, Cehao Yang, Ye Ma +7
Recently, large language models (LLMs) have demonstrated outstanding reasoning capabilities on mathematical and coding tasks. However, their application to financial tasks-especial…
GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation
Cehao Yang, Xiaojun Wu, Xueyuan Lin +6
Graph Retrieval-Augmented Generation (GraphRAG) enhances factual reasoning in LLMs by structurally modeling knowledge through graph-based representations. However, existing GraphRA…
Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval
Xiaojun Wu, Cehao Yang, Xueyuan Lin +6
Graph-based Retrieval-Augmented Generation (GraphRAG) has become the important paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing approa…
Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning
Cehao Yang, Xueyuan Lin, Xiaojun Wu +5
A practical approach to activate long chain-of-thoughts reasoning ability in pre-trained large language models is to perform supervised fine-tuning on instruction datasets synthesi…
Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models
Shengjie Ma, Xuhui Jiang, Chengjin Xu +3
Large Language Models (LLMs) have achieved remarkable success but remain data-inefficient, especially when learning from small, specialized corpora with limited and proprietary dat…
LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data
Cehao Yang, Xueyuan Lin, Chengjin Xu +5
Despite the growing development of long-context large language models (LLMs), data-centric approaches relying on synthetic data have been hindered by issues related to faithfulness…
Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
Shengjie Ma, Chengjin Xu, Xuhui Jiang +5
Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often f…
Financial Knowledge Large Language Model
Cehao Yang, Chengjin Xu, Yiyan Qi
Artificial intelligence is making significant strides in the finance industry, revolutionizing how data is processed and interpreted. Among these technologies, large language model…