5 citations · 5 across the 19 of their papers we have counts for
5 papers · 1 filter
AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
Ran Xu, Yuchen Zhuang, Zihan Dong +7
Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-p…
Contrastive Network Representation Learning
Zihan Dong, Xin Zhou, Ryumei Nakada +2
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks…
Avoid Catastrophic Forgetting with Rank-1 Fisher from Diffusion Models
Zekun Wang, Anant Gupta, Zihan Dong +1
Catastrophic forgetting remains a central obstacle for continual learning in neural models. Popular approaches -- replay and elastic weight consolidation (EWC) -- have limitations:…
AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
Can Jin, Yang Zhou, Qixin Zhang +8
Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or search-based methods guided by static Proc…
Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing
Tianci Liu, Ruirui Li, Zihan Dong +6
Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outd…