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GRIP: Geometric Refinement and Adaptive Information Potential for Data Efficiency

arXiv:2603.00031v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is increasingly governed by data efficiency rather than raw scaling volume. However, existing selection methods often decouple global distribution balancing from local instance selection, compromising the hierarchical integrity of the training set. We introduce \textbf{GRIP} (Geometric Refinement and Adaptive Information Potential), a framework that unifies these dimensions by modeling the corpus as an information-dense geometric space. GRIP employs a \textbf{Rapid Adaptation Probe (RAP)} to quantify the information potential of semantic clusters, dynamically re-allocating the sampling budget to regions with the highest representation deficits. Subsequently, we perform Intra-Cluster Selection using a \textbf{length-rectified geometric prior} to counteract embedding density artifacts and preserve long-tail logical sequences. Extensive evaluations on Mixture-of-Experts (MoE) models up to 300B

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Changhao Wang, Jiaolong Yang, Xinhao Yao, Yunfei Yu, Peng Jiao, Lu Yu, Junpeng Fang, Riccardo Cantoro, Qing Cui, Jun Zhou · · 1 min read · 2 views
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arXiv:2603.00031v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is increasingly governed by data efficiency rather than raw scaling volume. However, existing selection methods often decouple global distribution balancing from local instance selection, compromising the hierarchical integrity of the training set. We introduce \textbf{GRIP} (Geometric Refinement and Adaptive Information Potential), a framework that unifies these dimensions by modeling the corpus as an information-dense geometric space. GRIP employs a \textbf{Rapid Adaptation Probe (RAP)} to quantify the information potential of semantic clusters, dynamically re-allocating the sampling budget to regions with the highest representation deficits. Subsequently, we perform Intra-Cluster Selection using a \textbf{length-rectified geometric prior} to counteract embedding density artifacts and preserve long-tail logical sequences. Extensive evaluations on Mixture-of-Experts (MoE) models up to 300B tokens demonstrate that GRIP consistently outperforms state-of-the-art baselines, \textbf{surpassing the performance of models trained on $3\times$ larger uncurated datasets}. Our work establishes a robust geometric foundation for adaptive data curation in large-scale pre-training.

Executive Summary

This article introduces GRIP (Geometric Refinement and Adaptive Information Potential), a novel framework for large language models (LLMs) that enhances data efficiency by unifying global distribution balancing and local instance selection. GRIP models the corpus as an information-dense geometric space and employs a Rapid Adaptation Probe (RAP) to dynamically re-allocate the sampling budget. The framework is evaluated on Mixture-of-Experts (MoE) models up to 300B tokens, demonstrating superior performance compared to state-of-the-art baselines. The work establishes a robust geometric foundation for adaptive data curation in large-scale pre-training, offering significant benefits for model development and deployment.

Key Points

  • ▸ GRIP framework unifies global distribution balancing and local instance selection for data efficiency
  • ▸ Rapid Adaptation Probe (RAP) dynamically re-allocates sampling budget based on information potential
  • ▸ Intra-Cluster Selection using length-rectified geometric prior preserves long-tail logical sequences

Merits

Enhanced Data Efficiency

GRIP framework optimizes data selection, reducing the need for large-scale datasets and improving model performance

Improved Model Generalizability

GRIP's adaptive information potential and geometric refinement enable models to generalize better to diverse tasks and datasets

Scalability and Robustness

GRIP's geometric foundation and RAP enable efficient and robust data curation for large-scale pre-training

Demerits

Complexity and Implementation Challenges

GRIP's framework may be computationally intensive and require significant implementation efforts for practical application

Limited Evaluation on Real-World Tasks

While GRIP is evaluated on MoE models, its performance on real-world tasks and datasets remains to be fully explored

Potential Overfitting Risks

GRIP's adaptive information potential may lead to overfitting if not carefully calibrated and monitored

Expert Commentary

The GRIP framework presents a significant advancement in data efficiency for large language models. By unifying global distribution balancing and local instance selection, GRIP offers a more robust and scalable approach to data curation. However, its implementation challenges and potential overfitting risks must be carefully addressed. Further evaluation on real-world tasks and datasets is necessary to fully explore the benefits and limitations of GRIP. Nonetheless, this work has the potential to transform the field of AI development and deployment.

Recommendations

  • ✓ Further research is needed to explore the practical applications of GRIP in real-world tasks and datasets
  • ✓ Implementation challenges and potential overfitting risks should be carefully addressed through rigorous testing and validation

Sources

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