implementation detail · filed under optimization
Hybrid Muon and AdamW parameter-group optimizer assignment
- source
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- model
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- lab adopt it
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- strongest
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How sources treat it
One count per evidence span, weakest treatment to strongest.
default 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
the input and n-gram embeddings, output head, MoE router and the low-rank projections of GR stay on AdamW
defaultunclearin Qwen3.8-Flash-NextQwen
Filed alongside
Other methods under optimization :: optimizer.
MuonPer-Head MuonAdamWCategory-specific assignment of Muon and AdamWSplit fused gradients before orthogonalizationNesterov momentumSinkhorn-balanced updateAdam without weight decay for the N-gram embedding tableAdamW for attention and GDN output gatesAdamW for gated-residual low-rank projectionsAdamW for input embeddings and output headAdamW for the MoE routerAsynchronous Micro-Group pipelineCanzonaCUDA graph capture of the optimizer stepEight-step Newton–Schulz iterationHybrid Newton–Schulz iterationsHybrid optimization with Muon and AdamMoonlight-style learning-rate scalingMuon orthogonalization accuracy refinementMuon restricted to two-dimensional linear-map weightsMuon SplitMuownPeer-to-peer shard retrieval for Muon orthogonalization