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HyScale system accelerates long-context LLM training with sparse attention

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HyScale, a new training system designed to optimize the process of training large language models with long contexts, specifically focusing on learned sparse attention mechanisms. This system addresses the computational bottleneck in selecting relevant tokens for attention by introducing HyTopK for on-the-fly selection and selection replay for efficient recomputation. HyScale has demonstrated significant speedups, achieving up to 2.07x faster training for models like GLM-5.1 with million-token sequences compared to existing systems. AI

IMPACT Optimizes training for long-context models, potentially enabling more capable and efficient AI systems.

RANK_REASON The cluster contains a research paper detailing a new system for training AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

HyScale system accelerates long-context LLM training with sparse attention

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The cluster contains a research paper detailing a new system for training AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yangsheng Deng, Xinjing Huang, Yulong Wang, Yunzhuo Liu, Jun Hu, Shuyong Tang, Junchi Zeng, Mingji Han, Ray Ying, Key Zhang, Luo Mai ·

    HyScale: Scaling Long-Context Training for Learned Sparse Attention

    arXiv:2610.32057v1 Announce Type: new Abstract: We present HyScale, a long-context training system for learned sparse attention. Learned sparse attention is a promising architecture for million-token contexts, widely adopted in production since DeepSeek-V3.2. Selection, however, …

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