implementation detail · filed under model architecture
Pre-LN
A normalization placement that applies normalization before the relevant model block.
Also called Pre-LN (pre-normalization) placement, Pre-LN placement.
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How sources treat it
One count per evidence span, weakest treatment to strongest.
used 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
Similar to GPT-2 we use Pre-LN placement [7][8].
usedmodel architecturein gpt-oss-120b and gpt-oss-20bOpenAI
Filed alongside
Other methods under model architecture :: normalization & residual.
Gated ResidualManifold-Constrained Hyper-ConnectionsAttention Residuals (AttnRes)GatedNormIdentity Hyper-ConnectionsRMSNormSingle-Pass mHCBlock AttnResHyper-ConnectionsIndependent Per-Branch NormalizationPer-Branch Scalar Write GateQK-ClipAll-branch Elementwise Read GateBounded Positive GatesData-Dependent Residual Read and Write OperatorsDynamic Gating of Residual Reads and WritesElementwise Data-Dependent Residual Read GateFour-Branch Residual StreamFour-Stream Hyper-Connection Combine UpdateFull Attention ResidualsLow-Rank Gated MixPost-Embedding RMSNormSimplified AltUpSparse Gated Residual Writes