Verified claim · AI-ML · 92% confidence
RMSNorm (Root Mean Square Layer Normalization) introduced in paper: Root Mean Square Layer Normalization (Zhang & Sennrich, 2019).
Last verified 2026-05-31 · Methodology veritas-v0.1 · c64636dc60b1216f
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Structured fields
- Subject
- RMSNorm (Root Mean Square Layer Normalization)
- Predicate
introduced_in_paper- Object
- Root Mean Square Layer Normalization (Zhang & Sennrich, 2019)
- Confidence
- 92%
- Tags
- rmsnorm · root-mean-square-layer-normalization · normalization · layernorm · architecture · zhang · 2019
Sources (3)
[1] preprint · arXiv (Biao Zhang, Rico Sennrich — Edinburgh + Zurich) · 2019-10-16
Root Mean Square Layer Normalization“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.”
[2] github release · Biao Zhang (bzhangGo) · 2019-10-16
RMSNorm reference implementation[3] docs · Hugging Face
RMSNorm (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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// "RMSNorm (Root Mean Square Layer Normalization) introduced in paper: Root Mean Square Layer Normalization (Zhang & Sennrich, 2019)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/c64636dc60b1216f.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "RMSNorm (Root Mean Square Layer Normalization) introduced in paper: Root Mean Square Layer Normalization (Zhang & Sennrich, 2019)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_rmsnorm_root_mean_square_layer_normalization_fact() -> dict:
"""Fetch the verified SourceScore claim for RMSNorm (Root Mean Square Layer Normalization)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/c64636dc60b1216f.json")
return r.json()