$μ$pscaling small models: Principled warm starts and hyperparameter transfer
$μ$pscaling small models: Principled warm starts and hyperparameter transfer explores Develop a method for upscaling small neural networks to larger sizes with efficient hyperparameter transfer.. Commercial viability score: 4/10 in Model Upscaling.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K 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.
Related Papers
- Builds On ThisDeriving Hyperparameter Scaling Laws via Modern Optimization TheoryViability: 2.0
- Builds On ThisScaling Embeddings Outperforms Scaling Experts in Language ModelsViability: 3.0
- Builds On ThisOn the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter TransferViability: 3.0
- Prior WorkOn Neural Scaling Laws for Weather Emulation through Continual TrainingViability: 4.0
- Prior WorkThe Depth Delusion: Why Transformers Should Be Wider, Not Deeper