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20242026
most citedUnderstanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity

2 citations · 2 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

MARA: Flow-Matching-Guided Multi-Agent Resource Allocation for Computational Resource Efficient Learning

Hanye Zhao, Muning Wen, Yong Yu +1

Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown. E…

cs.LG2026

Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis

Yujie Zheng, Zhuo Li, Shengtao Zhang +8

Deploying Large Language Models to data-scarce programming domains poses significant challenges, particularly for kernel synthesis on emerging Domain-Specific Architectures where a…

cs.LG2025

PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning

Kun Hu, Muning Wen, Xihuai Wang +5

Multi-agent reinforcement learning (MARL) faces challenges in coordinating agents due to complex interdependencies within multi-agent systems. Most MARL algorithms use the simultan…

cs.LG2024

Hammer: Robust Function-Calling for On-Device Language Models via Function Masking

Qiqiang Lin, Muning Wen, Qiuying Peng +8

Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing the…

cs.LG2024

Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement

Muning Wen, Junwei Liao, Cheng Deng +3

Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. Traditional approaches often depend on meticulously designed prompts, hi…

cs.LG2024

Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Xidong Feng, Ziyu Wan, Muning Wen +4

Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step reaso…