arXiv (All)
open access
Amortizing Scaling Law Construction Costs
Scaling laws guide the design choices for training large foundation models, but deriving them involves training an exhaustive grid over hyperparameters, token budgets, and parameter counts, which is computationally expensive. Fitting a scaling law, however, only requires the best-loss frontier acros
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- From PhysioNet to foundation models—a history and potential futures.#472548
- AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models#203220
- Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology.#239824
- SpatialBench: Is Your Spatial Foundation Model an All-Round Player?#232893
- The Paton System as a Viability-First Framework for Scientific Explanation#105158
Record · ID 971973
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive