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OneLA: Scaling Linear-Attention Decoding to Large Beams in Generative Recommendation
Generative recommendation (GR) relies on large-beam decoding to generate hundreds of candidate items, creating a new scaling challenge for recurrent linear attention. Existing linear attention serving systems either materialize a full recurrent state for every beam or repeatedly replay shared histor
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