13 citations · 14 across the 6 of their papers we have counts for
6 papers
QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation
Xinguo Zhu, Shaohui Peng, Jiaming Guo +10
Developing high-performance GPU kernels is critical for AI and scientific computing, but remains challenging due to its reliance on expert crafting and poor portability. While LLMs…
Code Driven Planning with Domain-Adaptive Critic
Zikang Tian, Shaohui Peng, Du Huang +11
Large Language Models (LLMs) have been widely adopted as task planners for AI agents in sequential decision-making problems, leveraging their extensive world knowledge. However, th…
QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm
Qirui Zhou, Shaohui Peng, Weiqiang Xiong +11
The attention operator remains a critical performance bottleneck in large language models (LLMs), particularly for long-context scenarios. While FlashAttention is the most widely u…
QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives
Xuzhi Zhang, Shaohui Peng, Qirui Zhou +12
Computation-intensive tensor operators constitute over 90\% of the computations in Large Language Models (LLMs) and Deep Neural Networks.Automatically and efficiently generating hi…
Online Prototype Alignment for Few-shot Policy Transfer
Qi Yi, Rui Zhang, Shaohui Peng +10
Domain adaptation in reinforcement learning (RL) mainly deals with the changes of observation when transferring the policy to a new environment. Many traditional approaches of doma…
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
Shaohui Peng, Xing Hu, Rui Zhang +9
Hierarchical reinforcement learning (HRL) effectively improves agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e…