7 papers · 1 filter
Deep Research Pretraining via Predictive Navigation
Jiang Zhou, Zhiyuan Fan, Xing Wu +3
Deep research agents are often trained on expensive, environment-grounded tool-use trajectories that require repeated retrieval, document inspection, and report evaluation. We intr…
From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
Yeyong Yu, Wenya Hu, Xing Wu +1
As candidate generation and high-throughput experimentation advance, the primary bottleneck in materials discovery is shifting from property prediction to making reliable evaluatio…
WRAP++: Web discoveRy Amplified Pretraining
Jiang Zhou, Yunhao Wang, Xing Wu +2
Synthetic data rephrasing has emerged as a powerful technique for enhancing knowledge acquisition during large language model (LLM) pretraining. However, existing approaches operat…
Put the Space of LoRA Initialization to the Extreme to Preserve Pre-trained Knowledge
Pengwei Tang, Xiaolin Hu, Yong Liu +4
Low-Rank Adaptation (LoRA) is the leading parameter-efficient fine-tuning method for Large Language Models (LLMs), but it still suffers from catastrophic forgetting. Recent work ha…
LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
Ziyang Chen, Xing Wu, Junlong Jia +4
The rapid expansion of context length in large language models (LLMs) has outpaced existing evaluation benchmarks. Current long-context benchmarks often trade off scalability and r…
LiteLong: Resource-Efficient Long-Context Data Synthesis for LLMs
Junlong Jia, Xing Wu, Chaochen Gao +8
High-quality long-context data is essential for training large language models (LLMs) capable of processing extensive documents, yet existing synthesis approaches using relevance-b…