DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data | Paper Citation Record

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models , year=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data On Predicting the Post-training Potential of Pre-trained LLMs

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data LIMO: Less is More for Reasoning

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Textbooks Are All You Need

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Advances in Neural Information Processing Systems , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Advances in Neural Information Processing Systems , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Journal of machine learning research , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Scaling Language Models: Methods, Analysis & Insights from Training Gopher

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data QuRating: Selecting High-Quality Data for Training Language Models

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data DataMan: Data Manager for Pre-training Large Language Models

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Advances in Neural Information Processing Systems , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data arXiv preprint arXiv:2506.04689 , year=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data arXiv preprint arXiv:2510.10681 , year=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Kimi K2: Open Agentic Intelligence

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data arXiv preprint arXiv:2505.02881 , year=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data arXiv preprint arXiv:2602.07824 , year=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Qwen3 Technical Report

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data GLM-5: from Vibe Coding to Agentic Engineering

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data arXiv preprint arXiv:2603.14420 , year=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data ACM Transactions on Information Systems , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Advances in neural information processing systems , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data International Conference on Learning Representations , volume=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data MegaMath: Pushing the Limits of Open Math Corpora

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data DsDm: Model-Aware Dataset Selection with Datamodels

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Proceedings of the twelfth language resources and evaluation conference , pages=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data SemDeDup: Data-efficient learning at web-scale through semantic deduplication

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Proceedings of the 15th conference of the European chapter of the association for computational linguistics: volume 2, short papers , pages=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data A Bitter Lesson for Data Filtering

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Proceedings of the 57th annual meeting of the association for computational linguistics , pages=

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DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Measuring Mathematical Problem Solving With the MATH Dataset

Reference 59

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Observation a0ef692a-135d-452f-a08a-0dbf56024a89 · outbound

This paper cites International Conference on Learning Representations , volume=.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data International Conference on Learning Representations , volume=

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:47.339830Z

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Observation 37aa6d2a-baab-4990-9c0a-70ce9ba7b090 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:47.344071Z

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Unavailable: canonical work link unavailable.

Observation 0d7c2881-e612-4a91-989e-d076e7ae6252 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Advances in Neural Information Processing Systems , volume=

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:47.348680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8871fd0c-994f-4498-a8ee-052cb5f474a4 · outbound

This paper cites SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:47.352650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b81afe7-1eed-40ff-ad12-a250b1834e3b · outbound

This paper cites 2024 , eprint=.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data 2024 , eprint=

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:47.357054Z

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Unavailable: canonical work link unavailable.

Observation 3b84c7ac-d6c2-40c4-97b6-b98572751f00 · outbound

This paper cites Scaling Laws for Neural Language Models.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Scaling Laws for Neural Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:47.361651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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