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World of Software > Computing > The Impact of Data Size on Transformer Training: Overfitting & Loss Dynamics | HackerNoon
Computing

The Impact of Data Size on Transformer Training: Overfitting & Loss Dynamics | HackerNoon

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Last updated: 2025/06/21 at 9:11 PM
News Room Published 21 June 2025
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Table of Links

Abstract and 1 Introduction

2 Related Work

3 Model and 3.1 Associative memories

3.2 Transformer blocks

4 A New Energy Function

4.1 The layered structure

5 Cross-Entropy Loss

6 Empirical Results and 6.1 Empirical evaluation of the radius

6.2 Training GPT-2

6.3 Training Vanilla Transformers

7 Conclusion and Acknowledgments

Appendix A. Deferred Tables

Appendix B. Some Properties of the Energy Functions

Appendix C. Deferred Proofs from Section 5

Appendix D. Transformer Details: Using GPT-2 as an Example

References

6.2 Training GPT-2

Figure 4: Vanilla Transformers trained on the 2M Question-Formation dataset following the settings in (Murty et al., 2023). The training losses stabilize at a value of approximately 1, which corroborates the result presented in Proposition 4.

:::info
Authors:

(1) Xueyan Niu, Theory Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd.;

(2) Bo Bai baibo ([email protected]);

(3) Lei Deng ([email protected]);

(4) Wei Han ([email protected]).

:::


:::info
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

:::

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