From the 1 of 16 linked papers with an AI index.
16 papers
Scheduling Mixed RL Rollouts Beyond Prefix Locality
Zetao Hong, Song Yuan, Yuanhao Ding +4
Modern reinforcement learning (RL) post-training pipelines for large language models (LLMs) increasingly combine rollout workloads across multiple domains and feedback paradigms. P…
MoE Proxy Models for Low-Cost Failure Reproduction and Diagnosis in LLM RL Post-Training
Yikai Wang, Chuansai Zhou, Yuhang Zhou +10
Reinforcement learning (RL) post-training of large language models (LLMs) is computationally intensive and involves complex system pipelines with substantial debugging overhead. In…
Adaptive Matrix Multiplication for Dynamic Shapes on Ascend NPUs
Yuhang Zhou, Jiang Peng, Qianyu Jiang +7
Matrix Multiplication (MatMul) faces a "generalization crisis" driven by highly dynamic tensor shapes. This crisis is particularly acute on Ascend NPUs, where explicitly controlled…
TIDE-MC: Two-Sided Interpolative Decomposition for Billion-Scale GPU Matrix Completion
Chengying Huan, Yubo Wang, Pinhuan Wang +11
Matrix completion supports large-scale recommendation and scientific computing, yet existing GPU solvers commonly assume that the observed matrix or its dense factors fit in device…
SpecLA: Efficient Speculative Decoding for Linear-Attention Models
Zhibin Wang, Xuying Han, Zhaohua Yang +5
Linear-attention models replace the growing KV cache with recurrent states, but autoregressive decoding still reads, updates, and writes these states one token at a time. Speculati…
SmartSwap: Swap-Based Memory Optimization for LLM Training under Varying Operator Sequences
Zibo Wang, Yuhang Zhou, Zhibin Wang +13
The paper introduces SmartSwap, a swap‑based memory optimization framework that adapts to varying operator sequences in eager‑mode training of large language models, using a lightw…