5 papers
Large Language Models Explore by Latent Distilling
Yuanhao Zeng, Ao Lu, Lufei Li +3
Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limit…
Grad2Reward: From Sparse Judgment to Dense Rewards for Improving Open-Ended LLM Reasoning
Zheng Zhang, Ao Lu, Yuanhao Zeng +5
Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant breakthroughs in complex LLM reasoning within verifiable domains, such as mathematics and programmin…
Graph Foundation Models for Recommendation: A Comprehensive Survey
Bin Wu, Yihang Wang, Yuanhao Zeng +7
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role i…
DELIA: Diversity-Enhanced Learning for Instruction Adaptation in Large Language Models
Yuanhao Zeng, Fei Ren, Xinpeng Zhou +2
Although instruction tuning is widely used to adjust behavior in Large Language Models (LLMs), extensive empirical evidence and research indicates that it is primarily a process wh…
Token-Efficient Leverage Learning in Large Language Models
Yuanhao Zeng, Min Wang, Yihang Wang +1
Large Language Models (LLMs) have excelled in various tasks but perform better in high-resource scenarios, which presents challenges in low-resource scenarios. Data scarcity and th…