Methods › General › Normalization › RMSNorm

Root Mean Square Layer Normalization

RMSNorm

14 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability. RMSNorm is computationally simpler and thus more efficient than LayerNorm.

Source: Root Mean Square Layer Normalization

Papers archive 2025-07-28

14 shown of 14, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Arithmetic Reasoning1
Beat Tracking1
Code Generation1
Downbeat Tracking1
Drum Transcription1
Drum Transcription in Music (DTM)1
HellaSwag1
Language Modelling1
Mamba1
Math Word Problem Solving1
Mixture-of-Experts1
Model Compression1
Multi-Task Learning1
Multi-instrument Music Transcription1
Multi-task Language Understanding1
Multiple Choice Question Answering (MCQA)1
Music Transcription1
Quantization1
Question Answering1
Sentence Completion1

Usage over time archive 2025-07-28

Papers per year tagged with RMSNorm: 2019 to 2025, peak 8 8 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 2 papers 2023 2024: 8 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (14 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Normalization

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