3 citations · 10 across the 12 of their papers we have counts for
5 papers · 1 filter
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
Chenyang Song, Weilin Zhao, Xu Han +3
While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory-acc…
FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
Qingchuan Zhang, He Cao, Hao Li +6
Molecular optimization seeks to improve a molecule through small structural edits while preserving similarity to the starting compound. Recent language-model approaches typically t…
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
Yaxuan Li, Yuxin Zuo, Bingxiang He +8
On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly understood. This paper provides a sy…
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang +22
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…
ReLU Wins: Discovering Efficient Activation Functions for Sparse LLMs
Zhengyan Zhang, Yixin Song, Guanghui Yu +7
Sparse computation offers a compelling solution for the inference of Large Language Models (LLMs) in low-resource scenarios by dynamically skipping the computation of inactive neur…