Challenges in the construction of effective pretering data mixture
As a large language model (LLMS) scale in size and capacity, the option of preparation data remains an important determinant of downstream performance. Most LLMs are trained on common, web-scal datasets such as a common crawl, providing wide coverage but a clear domain label is lacking. This introduces difficulties in curating mixtures that balance general knowledge with domain-specific expertise.
The manual dataset curation, as seen in efforts such as pile, is labor-intensive and not well on a scale. In addition, the non-relationship relationship between data composition and model performance makes it non-batch to determine which proportions of domain data are optimal. These obstacles induce the need for automatic, scalable and adaptive data selection methods.
Climbing: A recurrence framework for data mixing search
To address it, NVIDIA researchers proposed climb,Clustering-based recurrence data mix bootstrapping-The framework that automates the discovery and refinement of data mixture for language model pretering. The climb combines uncontrolled clustering with recurrent adaptation to identify well-suited mixtures for general or domain-specific purposes.
The pipeline begins by pretending large -scale text data in a semantic space, which occurs in a semantic location using encoders. K-instrument clustering is then applied to organize data into coherent groups, which are sorted and merged on the basis of quality and excess of material. This candidate forms the basis of the construction of mixtures.
Subsequently, the climb uses the proxy model to evaluate the sample mixture and fit a regression-based prophet (eg, lightgbm) to estimate the mixture. A recurrence bootstrapping process refines progressively sampling, preference to high -performance configurations. This allows to convert on an effective data mixture under a fixed calculation budget.
Technical details and design ideas
The adaptation process is designed as a bia-level problem: at the lower level, the proxy model is trained on the candidate mixture; At the upper level, a prophet is learned to approximate the results of the performance. This prophet guides further samples and sorting, enabling the efficient exploration of the mixture space.
The climb mixture supports sparsity in weight, compact, domain-packed data encourages the discovery of the most. Clustering of clustering on embeding-token-level characteristics instead of cementic consistency within-groups. Recurring refinement is structured to balance the width (search location coverage) with depth (explicit accuracy), and ablation studies confirm that careful calculation allocation in recurrence improves allocation allocation allocation and improving the final performance.
Framework proxy models also exhibit strength in size and cluster granularities. While large proxy models make a little better predictions, even small models preserve major structural trends. Similarly, the climb is relatively insensitive to the initial cluster calculation, provided that it is within a reasonable range.
Empirical assessment and observation
The climb was evaluated on several general logic functions, including PIQA, ARC (easy and challenge), Hellaswag and Winogrande. A 1B-parameter model trained on climbing mixtures obtains average accuracy 60.41%Better comparable base lines such as Darmi and Regmix.
When extended to 400 B-token preteering, this 1B model improved the Lama-3.2-1 B on a wide suit of benchmark by 2.0%. Similarly, in the sub -500 meter model category, climb -based pretraying continuously improved models such as smallm and tinillama.
Domain expertise further highlights the utility of climbing. In STEM, Humanities and Social Sciences targeted MMLU benchmark, the climb-propelled model improved both random selection and complete search baseline. The recurring process showed frequent benefits at each stage, indicating effective guidance from the future model.
To facilitate breeding and further research, NVIDIA has released two resources:
- Climblab: A 1.2-trilian-token corpus was held in 20 cementic groups.
- Climbing: A 400 billion-token customized mixture for efficient pretering.
Trained trained people trained on climmics such as model Nemotron-CCs trained on dataset and demonstrated smallm, better scaling characteristics under the equivalent token budget.
conclusion
The climb offers a systematic approach to optimizing the data mixture in LLM pretering. By combining cementic clustering with proxy-based repetition discovery, it avoids dependence on manual annotations or static hyuristics. The method supports both general and expert training goals and is compatible with different calculations and data obstacles.
This structure contributes to the ongoing efforts in data-centered AI, which offers a scalable and royal option for data pipelines. Its empirical display model underlines the importance of data mix optimization in maximizing the utility, especially under the fixed resource budget.
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