arXiv (All)
open access
Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle
Data strategy plays a central role in training large language models (LLMs). It governs the selection, weighting, and scheduling of training data, thereby determining the capabilities a model can acquire from a fixed data budget. We treat high-quality data as having high training-dependent utility,
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
- Understanding Data Temporality Impact on Large Language Models Pre-training#216858
- Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models#1064054
- Don't Forget! Decomposing the Training Dynamics of Memorization in Language Models#1129542
- Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model#451351
- Latent Performance Profiling of Large Language Models#1233870
Record · ID 1357336
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