View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $10K - $14K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (64)

[1]
In Search of Adam's Secret Sauce
2025Antonio Orvieto, Robert Gower
[2]
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
2025Tianjin Huang, Ziquan Zhu et al.
[3]
Taming LLMs with Gradient Grouping
2025Siyuan Li, Juanxi Tian et al.
[4]
APOLLO: SGD-like Memory, AdamW-level Performance
2024Hanqing Zhu, Zhenyu (Allen) Zhang et al.
[5]
Cautious Optimizers: Improving Training with One Line of Code
2024Kaizhao Liang, Lizhang Chen et al.
[6]
CompAct: Compressed Activations for Memory-Efficient LLM Training
2024Yara Shamshoum, Nitzan Hodos et al.
[7]
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis
2024Weronika Ormaniec, Felix Dangel et al.
[8]
SOAP: Improving and Stabilizing Shampoo using Adam
2024Nikhil Vyas, Depen Morwani et al.
[9]
BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models
2024Qi Luo, Hengxu Yu et al.
[10]
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
2024Rui Pan, Xiang Liu et al.
[11]
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
2024Jiawei Zhao, Zhenyu (Allen) Zhang et al.
[12]
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
2024Frederik Kunstner, Robin Yadav et al.
[13]
Why Transformers Need Adam: A Hessian Perspective
2024Yushun Zhang, Congliang Chen et al.
[14]
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
2024Yihua Zhang, Pingzhi Li et al.
[15]
Spike No More: Stabilizing the Pre-training of Large Language Models
2023Sho Takase, Shun Kiyono et al.
[16]
NEFTune: Noisy Embeddings Improve Instruction Finetuning
2023Neel Jain, Ping-yeh Chiang et al.
[17]
Linear attention is (maybe) all you need (to understand transformer optimization)
2023Kwangjun Ahn, Xiang Cheng et al.
[18]
Normalization Layers Are All That Sharpness-Aware Minimization Needs
2023Maximilian Mueller, Tiffany J. Vlaar et al.
[19]
Toward Understanding Why Adam Converges Faster Than SGD for Transformers
2023Yan Pan
[20]
Transformers learn in-context by gradient descent
2022J. Oswald, Eyvind Niklasson et al.

Showing 20 of 64 references

Founder's Pitch

"Develop Magma, a Momentum-aligned gradient masking optimizer to enhance LLM training efficiency."

LLM TrainingScore: 3View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

0/4 signals

0

Series A Potential

1/4 signals

2.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/17/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.