Quantitative Bounds for Length Generalization in Transformers

ICLROral2026

Authors
Zachary Izzo, Eshaan Nichani, Jason D. Lee
Affiliation
NEC Labs America
Venue
ICLR 2026
Track
Oral

TL;DR

We provide an upper bound on the length of training sequences required for a transformer to generalize to sequences of arbitrary lengths.

Opening excerpt from the authors’ abstract. source

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Topics

generalization transformer

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