Perplexity-Aware Scaling Law Boosts Data Efficiency in Continual Pre‑Training

Global: Perplexity-Aware Scaling Law Boosts Data Efficiency in Continual Pre‑Training

Key Development

Researchers have introduced a perplexity‑aware data scaling law designed to enhance the efficiency of continual pre‑training (CPT) for large language models by enabling adaptive selection of high‑utility data subsets.

Background on Continual Pre‑Training

Continual pre‑training adapts foundation models to domain‑specific tasks, and prior scaling laws have established a power‑law relationship between dataset size and test loss. However, the incremental benefits of simply increasing data volume diminish rapidly, leading to suboptimal data utilization.

Proposed Methodology

The new approach leverages the perplexity measured by a pre‑trained model on domain data as a proxy for the knowledge gap, establishing a predictive link between the perplexity landscape of candidate samples and the resulting test loss.

Adaptive Data Selection

By fitting the scaling law across diverse perplexity regimes, the method prioritizes content that maximizes knowledge absorption while minimizing redundancy and noise, thereby selecting near‑optimal training subsets.

Experimental Validation

Extensive experiments reported in the study indicate that the technique consistently identifies high‑utility subsets and delivers superior performance on both medical and general‑domain benchmarks compared with conventional CPT strategies.

Implications for Model Training

According to the authors, the scaling law improves data efficiency, potentially reducing computational costs and accelerating the deployment of domain‑adapted language models.

Future Directions

The authors suggest that further research could explore extending the perplexity‑aware framework to other model architectures and scaling scenarios.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.