RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference

arXiv:2602.18196v4 Announce Type: replace Abstract: Structured dilated attention has an appealing inference-time efficiency knob: it reduces the FLOPs of attention and the KV cache size by a factor of the dilation size D, while preserving long-range connectivity. While prior work studies it by training each configuration from scratch, directly sparsifying a pretrained attention model into a dilated pattern leads to severe accuracy degradation, preventing flexible reuse across inference scenarios. We introduce RAT+, a dense-pretraining architecture that augments attention with full-sequence rec

Source: arXiv cs.LG — read the full report at the original publisher.

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