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Optimizer-Stability-Risk-Dataset-LLM-Pretraining

Simulated LLM-pretraining loss-spike probability for 448 configurations across 7 optimizers (AdamW, naive/scaled/8-bit Muon, naive/stabilized SOAP, Gluon), 4 batch-size regimes, and 4 model scales, grounded almost entirely in 2025-2026 optimizer research (Muon, SOAP, Gluon). Forward-looking task predicts a loss spike by the end of training from only the first 10% of training steps.

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updated 12 Aug 2026

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COVERAGE India

LICENSE Attribution 4.0 International (CC BY- 4.0)

FORMAT Synthetic

TYPE Structured

SECTOR Science, Technology and Research

UPDATES NA

About Dataset

Simulates loss-spike risk grounded in the 2025 wave of matrix-aware optimizer research. 448 configs across 7 optimizers (naive vs. fixed Muon/SOAP variants, plus Gluon and 8-bit Muon), 4 batch regimes, 4 model scales (0.5B-70B). Ten sources, nine from 2025 and one a July 2026 technical report, in optimizer_stability_theory.py — including "Muon is Scalable for LLM Training" (Moonlight model paper), a formal Muon convergence analysis, and a unified SOAP/Muon/AdamW stability study identifying and fixing SOAP's large-batch loss spikes.

Dataset Metadata

License

Attribution 4.0 International (CC BY- 4.0)

Sector

Science, Technology and Research

Author

Jai Mali

Source Organisation

Symbiosis Skills and Professional University

Uploaded By

Data Quality Score (Beta)

4.5

Dataset Type

Structured

Data Type

Synthetic

Task Category

N.A.

Time Granularity

NA

Frequency

NA

Geographic Granularity

N.A.

Primary Key / Indicator

N.A.

Data Collection Method

N.A.

Language

N.A.

Year Range

N.A.

Date & Time

09/08/26 17:14:29

Visibility

Open

Hosted / Redirected

Hosted

Tags

Large Language Model

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