Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training

Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/does-your-optimizer-care-how-you-normalize-normalization-optimizer-coupling-in-llm-training

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Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID does-your-optimizer-care-how-you-normalize-normalization-optimizer-coupling-in-llm-training | Route /signal-canvas/does-your-optimizer-care-how-you-normalize-normalization-optimizer-coupling-in-llm-training

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/does-your-optimizer-care-how-you-normalize-normalization-optimizer-coupling-in-llm-training

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training

PDF: https://arxiv.org/pdf/2604.01563v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:41.059Z

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Not build-ready: Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training

/buildability/does-your-optimizer-care-how-you-normalize-normalization-optimizer-coupling-in-llm-training

Ignoreblocked

Subject: Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Derf suffers a large negative interaction with Muon, with its gap to RMSNorm growing from +0.31 nats under AdamW to +0.97 under Muon, approximately three times larger.
Implicationpartial
Explicitly stated in abstract with specific numeric values
Verificationpartial
partial
Evidencepartial
Dynamic Tanh (DyT; Zhu et al., 2025), included as a bounded-normalizer control, shows no such penalty.
Implicationpartial
Directly stated in abstract as a control comparison
Verificationpartial
partial
Evidencepartial
Our evidence points to two failure modes of erf under Muon's faster spectral-norm growth: saturation (lossy compression) and scale blindness (discarding activation magnitude).
Implicationpartial
Directly stated in abstract as explanation for observed results
Verificationpartial
partial
Evidencepartial
An EMA-blend that reintroduces running scale estimates recovers ~84% of the gap.
Implicationpartial
Explicitly stated in abstract with specific percentage
Verificationpartial
partial
Evidencepartial
Separately, reducing Derf's alpha from its published default (0.5 to 0.3) recovers ~80% by keeping erf in its near-linear regime, where it approximately preserves relative scale.
Implicationpartial
Explicitly stated in abstract with specific parameter values and percentage
Verificationpartial
partial
Evidencepartial
Using Derf's published default alpha with Muon incurs a 0.66-nat interaction penalty without producing NaNs or divergence.
Implicationpartial
Explicitly stated in abstract with specific numeric penalty
Verificationpartial
partial
Evidencepartial
making the failure easy to miss in short pilot runs.
Implicationpartial
Directly stated in abstract as implication of the findings
Verificationpartial
partial
Evidencepartial
In LLM training, normalization layers and optimizers are typically treated as independent design choices. In a 3x2 factorial at 1B parameters and 1000 training steps, we show this assumption can fail.
Implicationpartial
Strongly implied by the paper's findings and stated problem framing
Verificationpartial
partial

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Does Your Optimizer Care How You Normalize? Normalization-Optimizer Coupling in LLM Training

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