11 citations · 13 across the 7 of their papers we have counts for
7 papers
DLLG: Dynamic Logit-Level Gating of LLM Experts
Bingnan Li, Zhaoyang Zhang, Xiaoze Liu +6
Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensemblin…
Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models
Xiaoze Liu, Dhananjay Ram, Yuting Zhang +3
We introduce Mutual Reinforcement Learning, a framework for concurrent RL post-training in which heterogeneous LLM policies exchange typed experience while keeping separate paramet…
Asymmetric Actor-Critic for Multi-turn LLM Agents
Shuli Jiang, Zhaoyang Zhang, Yi Zhang +3
Large language models (LLMs) exhibit strong reasoning and conversational abilities, but ensuring reliable behavior in multi-turn interactions remains challenging. In many real-worl…
Reinforcement-aware Knowledge Distillation for LLM Reasoning
Zhaoyang Zhang, Shuli Jiang, Yantao Shen +6
Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such model…
Distributed Learning over Networks with Graph-Attention-Based Personalization
Zhuojun Tian, Zhaoyang Zhang, Zhaohui Yang +2
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model. However, due to the underlying non-i.i.d. data distribut…
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
Zhaoyang Zhang, Yantao Shen, Kunyu Shi +7
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, result…