3 citations · 7 across the 15 of their papers we have counts for
6 papers · 1 filter
OptiMAS: Automatically Optimize Multi-Agent System
Yuxin Cheng, Chang Liu, Hanxin Yu +9
Automated evolution of Multi-Agent Systems (MAS) holds significant potential for reducing the manual effort required to design and optimize LLM-based agent architectures. However,…
What Makes Interaction Trajectories Effective for Training Terminal Agents?
Sidi Yang, Chaofan Tao, Jierun Chen +11
Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficulty, harness design, and stude…
AIS: Adaptive Importance Sampling for Quantized RL
Jiajun Zhou, Wei Shao, Lingchao Zheng +2
Reinforcement learning (RL) for large language models (LLMs) is dominated by the cost of rollout generation, which has motivated the use of low-precision rollouts (e.g., FP8) paire…
ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems
Wenyong Zhou, Yuannuo Feng, Yizhe Chen +6
Large language models (LLMs) with mixture-of-experts (MoE) architectures achieve remarkable scalability by sparsely activating a subset of experts per token, yet their frequent exp…
The Art of Efficient Reasoning: Data, Reward, and Optimization
Taiqiang Wu, Zenan Xu, Bo Zhou +1
Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning, but also suffer from heavy computational overhead. To address this issue, efficient…
Nonparametric Teaching of Attention Learners
Chen Zhang, Jianghui Wang, Bingyang Cheng +7
Attention learners, neural networks built on the attention mechanism, e.g., transformers, excel at learning the implicit relationships that relate sequences to their corresponding…