Meta's looped transformer paper: 32 loops, saturation points, and a fix for parallel inference
iScienceLuvr · x · 2026-10-31
The full abstract of Meta FAIR's arXiv:2610.40325 (Chanakya Ekbote, Artidoro Pagnoni, Mike Lewis et al.) details how looping—reapplying the same transformer weights—deepens models without extra parameters. Full-transformer looping wins under a parameter budget, middle-block looping wins under a FLOPs budget, gains saturate or destabilize with too many loops, loop-specific low-rank adapters help more as loops grow, and naive Parallel Looped Transformers fail unless the current-token embedding is preserved and separately normalized before combination with the recurrent state.
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