3 citations · 4 across the 3 of their papers we have counts for
4 papers
CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL
Shouli Wang, Yanfeng Jia, Zhihao Ou +6
During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high rewards without improving the…
Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
Zhiqing Cui, Haotong Xie, Jiahao Yuan +11
Large language model (LLM) multi-agent systems typically rely on rigid orchestration, committing either to flat per-query routing or to hand-engineered task decomposition, so decom…
Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters
Yixin Song, Haotong Xie, Zhengyan Zhang +4
Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However…
PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU
Yixin Song, Zeyu Mi, Haotong Xie +1
This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key principle…