{"total":22,"rows":[{"id":"2603.18534","title":"Data-efficient pre-training by scaling synthetic megadocs","created":"2026-03-19","cited_by_count":7,"area":null},{"id":"2510.10681","title":"RePro: Training Language Models to Faithfully Recycle the Web for Pretraining","created":"2025-10-12","cited_by_count":6,"area":null},{"id":"2604.13977","title":"How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data","created":"2026-04-15","cited_by_count":6,"area":null},{"id":"2510.14113","title":"Toward Cybersecurity-Expert Small Language Models","created":"2025-10-15","cited_by_count":4,"area":null},{"id":"2511.07464","title":"Motif 2 12.7B technical report","created":"2025-11-07","cited_by_count":4,"area":null},{"id":"2603.06603","title":"Scale Dependent Data Duplication","created":"2026-02-18","cited_by_count":4,"area":null},{"id":"2508.01326","title":"Large-Scale Diverse Synthesis for Mid-Training","created":"2025-08-02","cited_by_count":3,"area":null},{"id":"2510.20860","title":"Data-Centric Lessons To Improve Speech-Language Pretraining","created":"2025-10-22","cited_by_count":3,"area":null},{"id":"2603.14420","title":"Data Darwinism Part II: DataEvolve -- AI can Autonomously Evolve Pretraining Data Curation","created":"2026-03-15","cited_by_count":3,"area":null},{"id":"2604.04204","title":"How Does \"English (US)\" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline","created":"2026-04-05","cited_by_count":3,"area":null},{"id":"2509.22947","title":"Induction Signatures Are Not Enough: A Matched-Compute Study of Load-Bearing Structure in In-Context Learning","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2605.17849","title":"Generating Pretraining Tokens from Organic Data for Data-Bound Scaling","created":"2026-05-18","cited_by_count":2,"area":null},{"id":"2603.01691","title":"Building a Strong Instruction Language Model for a Less-Resourced Language","created":"2026-03-02","cited_by_count":1,"area":null},{"id":"2607.24717","title":"DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data","created":"2026-07-27","cited_by_count":1,"area":null},{"id":"2509.24356","title":"Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2511.01090","title":"Improving Romanian LLM Pretraining Data using Diversity and Quality Filtering","created":"2025-11-02","cited_by_count":0,"area":null},{"id":"2603.24826","title":"Synthetic Rewriting as a Quality Multiplier: Evidence from Portuguese Continued Pretraining","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2604.06829","title":"WRAP++: Web discoveRy Amplified Pretraining","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2607.07766","title":"Alignment Plausibility: A New Standard for Assuring AI in Healthcare","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2608.28478","title":"Blind Men and the Elephant: Probing the Epistemic Myopia of LLMs under Long-Tail Divergent Knowledge","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.32484","title":"Towards Scalable Data Diversification for Language Model Pretraining via Leverage Score Sampling","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.34287","title":"ReScraper: Unified Scraping and Cleaning of Web Data for Effective LLM Pretraining","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}