Study: Minor architecture choices cripple long context capabilities
rohanpaul_ai · x · 2026-08-17
A new paper testing 26 comparable 7B models examines how four architecture choices—QK normalization, grouped-query attention (GQA), sliding-window attention (SWA), and pretraining context length—affect long context extension. It finds that architectures that perform well in short-context pretraining, particularly combinations like GQA and SWA, suffer significant degradation when extended to long contexts. Even extensive long-context training fails to close the gap caused by poor architecture. The paper suggests early-stage long-context stress testing.
Related event: COLM papers reveal common Transformer designs hurt long-context scaling(4 posts)→
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