5 papers
Multi-level context Modeling for consistent expert selection in Mixture-of-Experts
Shuhan Huang, Naifan Zhang, Yuanbo Tang +2
Mixture-of-Experts (MoE) enables efficient scaling of Transformer models by routing tokens to a small subset of experts. However, existing routers typically condition expert select…
MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue
Naifan Zhang, Ruihan Sun, Jinwei Su +4
Reinforcement learning (RL) for large language models (LLMs) has shown strong performance in single-turn tasks, but extending it to multi-turn interaction remains challenging due t…
Dy-mer: An Explainable DNA Sequence Representation Scheme using Dictionary Learning
Zhiyuan Peng, Naifan Zhang, Yuanbo Tang +1
DNA sequences encode critical genetic information, yet their variable length and discrete nature impede direct utilization in deep learning models. Existing DNA representation sche…
Echo-N1: Affective RL Frontier
Naifan Zhang, Ruihan Sun, Ruixi Su +9
The LLM field has spent a year perfecting RL for tasks machines already excel at, math, code, and deterministic reasoning, while completely sidestepping the domain that actually de…
Unveiling Hidden Collaboration within Mixture-of-Experts in Large Language Models
Yuanbo Tang, Yan Tang, Naifan Zhang +2
Mixture-of-Experts based large language models (MoE LLMs) have shown significant promise in multitask adaptability by dynamically routing inputs to specialized experts. Despite the…