1 citations · 1 across the 4 of their papers we have counts for
5 papers
SIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation
Zhenrui Yue, Huimin Zeng, Yueqi Wang +8
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for improving large language models (LLMs) on various tasks, yet its sparse outcome rewards lac…
FASA: Frequency-aware Sparse Attention
Yifei Wang, Yueqi Wang, Zhenrui Yue +6
The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address th…
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation
Yueqi Wang, Zhenrui Yue, Huimin Zeng +2
Despite recent advancements in language and vision modeling, integrating rich multimodal knowledge into recommender systems continues to pose significant challenges. This is primar…
Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood
Yueqi Wang, Zhankui He, Zhenrui Yue +2
In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR)…
Linear Recurrent Units for Sequential Recommendation
Zhenrui Yue, Yueqi Wang, Zhankui He +3
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time…