DLA by ByteDance & Universities Tackles Long-Context Costs with Dynamic Linear Attention
burkov · x · 2026-08-14
Long-context models face a tradeoff between the high computational cost of standard Transformer attention and the detail loss of cheaper linear attention due to fixed compression schedules. ByteDance, in collaboration with Ohio State University and the University of Michigan, introduced Dynamic Linear Attention (DLA).
The core mechanisms of DLA include:
- Input-dependent compression: It decides state retention based on how much a new token alters the current memory.
- Fine-grained retention: It keeps finer-grained states around significant changes while merging repetitive stretches.
- Predictable memory: It caps total stored states by merging neighboring states with low information density.
Experiments across 16 reasoning, retrieval, and long-context datasets show that DLA consistently outperforms fixed-schedule baselines like Log-Linear attention.
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