Long Context Pre-Training with Lighthouse Attention
Training causal transformers at extreme sequence lengths is bottlenecked by the quadratic time and memory of scaled dot-product attention (SDPA). In this work, we propose Lighthouse Attention, a training-only symmetrical selection-based hierarchical attention algorithm that wraps around ordinary SDPA and can be easily removed towards the end of the training. Our hierarchical selection is also gradient-free, which exempts us from dealing with a complicated and potentially inefficient backward pass kernel. Our contribution is three-fold: (i) A subquadratic hierarchical pre- and post-processing step that does adaptive compression and decompression of the sequence. (ii) A symmetrical compress...
| Authors | Bowen Peng, Subho Ghosh, Jeffrey Quesnelle |
|---|---|
| Primary category | cs.CL |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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