Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training cost, and accuracy. In this work, we show that full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation. Our approach is built on three observations: (1) only a small subset of attention heads truly requires full long-context processing; (2) long-range retrieval is governed primarily by a low-dimensional subspace, allowing relevant tokens to be re...
| Authors | Yanke Zhou, Yiduo Li, Hanlin Tang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 May 2026 |
| Last updated at source | 8 June 2026 |
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