1 citations · 1 across the 7 of their papers we have counts for
10 papers
LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning
Bowen Ping, Zijun Chen, Tingfeng Hui +4
Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on rew…
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…
On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
Tongcheng Zhang, Zhanpeng Zhou, Mingze Wang +4
One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient-based training algorithms. Motivated by empirical observation…
FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach
Ning Liao, Xiaoxing Wang, Xiaohan Qin +1
As revealed by the scaling law of fine-grained MoE, model performance ceases to be improved once the granularity of the intermediate dimension exceeds the optimal threshold, limiti…
JTok: On Token Embedding as another Axis of Scaling Law via Joint Token Self-modulation
Yebin Yang, Huaijin Wu, Fu Guo +5
LLMs have traditionally scaled along dense dimensions, where performance is coupled with near-linear increases in computational cost. While MoE decouples capacity from compute, it…
UniLabOS: An AI-Native Operating System for Autonomous Laboratories
Jing Gao, Junhan Chang, Haohui Que +12
Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separ…