Lighthouse Attention: The Training-Time Hierarchy That Makes Quadratic Attention Practical Again
The article discusses the Lighthouse Attention method, which enhances the efficiency of quadratic attention in machine learning. It introduces a two-stage training process that significantly speeds up pretraining without architectural changes. The method demonstrates improved performance in attention tasks while maintaining compatibility with existing frameworks.
- ▪Lighthouse Attention achieves a 1.4–1.7× speedup in pretraining wall-clock time against dense SDPA at larger contexts.
- ▪The method uses symmetric pyramid pooling to compress queries, keys, and values, resulting in a more efficient attention call.
- ▪A two-stage training process ensures recoverability and allows the model to outperform dense attention baselines.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1699525) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Susilo harjo Posted on May 19 • Originally published at susiloharjo.web.id Lighthouse Attention: The Training-Time Hierarchy That Makes Quadratic Attention Practical Again #ai #machinelearning TL;DR: 1.4–1.7× pretraining wall-clock speedup against dense SDPA at 32K–128K context — no inference overhead, no architectural changes.
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