Scaling Laws for Transfer (2102.01293v1)

Abstract: We study empirical scaling laws for transfer learning between distributions in an unsupervised, fine-tuning setting. When we train increasingly large neural networks from-scratch on a fixed-size dataset, they eventually become data-limited and stop improving in performance (cross-entropy loss). When we do the same for models pre-trained on a large language dataset, the slope in performance gains is merely reduced rather than going to zero. We calculate the effective data "transferred" from pre-training by determining how much data a transformer of the same size would have required to achieve the same loss when training from scratch. In other words, we focus on units of data while holding everything else fixed. We find that the effective data transferred is described well in the low data regime by a power-law of parameter count and fine-tuning dataset size. We believe the exponents in these power-laws correspond to measures of the generality of a model and proximity of distributions (in a directed rather than symmetric sense). We find that pre-training effectively multiplies the fine-tuning dataset size. Transfer, like overall performance, scales predictably in terms of parameters, data, and compute.

Scaling Laws for Transfer: A Systematic Exploration of Neural Model Generalization

This paper focuses on understanding the empirical scaling laws for transfer learning, specifically examining unsupervised model pre-training followed by fine-tuning across different data distributions. The authors investigate how model performance scales with increasing parameters, data, and compute resources when transferring knowledge from a large pre-training dataset to a much smaller, task-specific fine-tuning dataset.

Key Contributions

1. Effective Data Transfer Measurement: The research introduces a novel concept of "effective data transferred", which quantifies the data equivalent that would have been needed by a transformer model trained from scratch to achieve a similar loss as a pretrained model. This approach allows the characterization of the efficacy of transfer learning in a manner analogous to data equivalency.
2. Power-Law Scaling: The authors provide evidence that the effective data transferred in the low-data regime follows a predictable power-law function, characterized by parameters like model size and fine-tuning dataset size. The relationship is expressed through the equation $D_T = k(D_F)^\alpha(N)^\beta$, where $D_T$ is the effective data transferred, $D_F$ is the size of the fine-tuning dataset, $N$ is the model size, and $k$, $\alpha$, $\beta$ are empirical constants.
3. Model Architecture and Data Proximity: The paper finds that the exponent $\beta$ depends on the model architecture and target distribution, remaining consistent across different pre-training distributions, while $\alpha$ indicates the directed proximity between the pre-training and fine-tuning datasets.
4. Practical Implications: The results suggest that pre-training effectively multiplies the size of the fine-tuning dataset, especially in the low-data regime, which makes transfer learning highly advantageous when labeled data is scarce. For instance, transferring from text to Python data, the effective data multiplier decreases as more fine-tuning data becomes available, highlighting the diminishing returns effect.

Implications for AI Advancement

The exploration of these scaling laws proposes a theoretical framework for understanding the generality and adaptability of large neural models. The predictable scaling of transfer efficiency can inform decisions about model size vs. dataset collection, guiding more resource-efficient training practices. Moreover, the concept of exponent $\alpha$ as a measure of distributional closeness presents a pathway for designing curricula that optimize cross-domain transfer efficiency.

Challenges and Limitations

The paper acknowledges certain limitations, including the reliance on hyperparameters tuned for natural language processing tasks and the examination primarily in unsupervised settings. Additionally, the paper did not explore varied neural architectures beyond transformers nor did it address potential differences across supervised learning for reinforcement tasks.

Future Directions

The findings open avenues for further research into transfer learning, particularly focusing on extending scaling laws to supervised and reinforcement learning scenarios, exploring diverse model architectures beyond transformers, and achieving better empirical fits for pre-training across more distant domains. Additionally, there is interest in efficiently measuring transfer potential across more varied datasets, thereby aiding strategic decisions about data acquisition and model scaling.

In conclusion, this paper contributes significantly to the discourse on transfer learning in AI, offering a quantitative framework for understanding and optimizing the interplay between model capacity, dataset size, and cross-domain learning efficacy. These insights could serve as guidelines for designing future AI systems that leverage pre-training most effectively to achieve robust and scalable performance across tasks.