87 citations · 160 across the 49 of their papers we have counts for
5 papers · 1 filter
Spiking Graph Predictive Coding for Reliable OOD Generalization
Jing Ren, Jiapeng Du, Bowen Li +6
Graphs provide a powerful basis for modeling Web-based relational data, with expressive GNNs to support the effective learning in dynamic web environments. However, real-world depl…
EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable Learning
He Du, Bowen Li, Aijun Yang +3
Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distill…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
Shuo Yu, Yingbo Wang, Ruolin Li +7
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve…
Parameter Efficient Reinforcement Learning from Human Feedback
Hakim Sidahmed, Samrat Phatale, Alex Hutcheson +16
While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computa…