ARXIV:2603.14712 · LLM TRAINING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Towards Next-Generation LLM Training: From the Data-Centric Perspective

arXiv

Develop an agent-based system for automated data preparation and management in LLM training.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain Develop an agent-based system for automated data preparation and management in LLM training.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

Develop an agent-based system for automated data preparation and management in LLM training. Despite this success, the preparation and effective utilization of the massive datasets required for LLM training remain major bottlenecks.

METHOD

Full abstract

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks and domains, with data playing a central role in enabling these advances. Despite this success, the preparation and effective utilization of the massive datasets required for LLM training remain major bottlenecks. In current practice, LLM training data is often constructed using ad hoc scripts, and there is still a lack of mature, agent-based data preparation systems that can automatically construct robust and reusable data workflows, thereby freeing data scientists from repetitive and error-prone engineering efforts. Moreover, once collected, datasets are often consumed largely in their entirety during training, without systematic mechanisms for data selection, mixture optimization, or reweighting. To address these limitations, we advocate two complementary research directions. First, we propose building a robust, agent-based automatic data preparation system that supports automated workflow construction and scalable data management. Second, we argue for a unified data-model interaction training system in which data is dynamically selected, mixed, and reweighted throughout the training process, enabling more efficient, adaptive, and performance-aware data utilization. Finally, we discuss the remaining challenges and outline promising directions for future research and system development.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. First, we propose building a robust, agent-based automatic data preparation system that supports automated workflow construction and scalable data management.

WHY NOW

LLM Training moved forward this cycle; last verified April 2026. Public score 4.0/10.

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Opportunity summary

Score4.0

PainDevelop an agent-based system for automated data preparation and management in LLM training.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop an agent-based system for automated data preparation and management in LLM training.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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Towards Next-Generation LLM Training: From the Data-Centric Perspective

Towards Next-Generation LLM Training: From the Data-Centric Perspective

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