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Towards Optimal Learning of Language Models (2402.17759v2)

Published 27 Feb 2024 in cs.CL

Abstract: This work studies the general principles of improving the learning of LLMs (LMs), which aims at reducing the necessary training steps for achieving superior performance. Specifically, we present a theory for the optimal learning of LMs. We first propose an objective that optimizes LM learning by maximizing the data compression ratio in an "LM-training-as-lossless-compression" view. Then, we derive a theorem, named Learning Law, to reveal the properties of the dynamics in the optimal learning process under our objective. The theorem is then validated by experiments on a linear classification and a real-world LLMing task. Finally, we empirically verify that the optimal learning of LMs essentially stems from the improvement of the coefficients in the scaling law of LMs, indicating great promise and significance for designing practical learning acceleration methods. Our code can be found at https://aka.ms/LearningLaw.

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Authors (6)
  1. Yuxian Gu (21 papers)
  2. Li Dong (154 papers)
  3. Yaru Hao (16 papers)
  4. Qingxiu Dong (39 papers)
  5. Minlie Huang (226 papers)
  6. Furu Wei (291 papers)
Citations (7)
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Summary

  • The paper presents a new approach that treats language model training as lossless data compression to enhance learning efficiency.
  • It introduces the 'Learning Law' which dynamically re-weights data contributions to ensure uniform impact and prevent overfitting.
  • Empirical tests on Perceptron and Transformer models reveal significant reductions in training steps, indicating promising scalability.

Exploring Optimal Learning in LLMs Through Compression Ratio Maximization

Introduction to Optimal Learning Theory for LMs

The landscape of LLMs (LMs) has been profoundly reshaped with the evolution and introduction of LLMs. A pivotal concern in this development corridor is enhancing the learning efficiency of LMs—reducing training steps while preserving or even improving model performance. Our theory posits a novel approach to this concern by embedding the notion of data compression within the learning process of LMs, aiming at maximizing the compression ratio as the principal optimization objective.

Core Proposition: Maximizing Compression Ratio

Our method diverges from traditional model-level, optimizer-level, or data-level optimizations, proposing an "LM-training-as-lossless-compression" perspective. Here, we introduce the concept of minimizing the area under the curve (AUC) of the loss function, equating this minimization with maximizing data compression during the training phase. Highly compressed data signal an efficient learning process, marking the cornerstone of our optimization objective. This approach not only elevates performance but also aligns with the observed prowess of LMs in data generalization, laying a theoretical foundation hitherto not fully explored.

Unveiling the Learning Law

The Learning Law, an outcome of our research, delineates a fundamental property within the optimal learning trajectory under the proposed optimization objective. It establishes that, in an ideal learning setting, every data point contributes equally to the learning algorithm. This revelation brings to light a dynamic data re-weighting strategy inherent in the optimal learning policy, mirroring the best teaching methods found in educational psychology. It dynamically encourages learning from highly contributive examples and prevents overfitting, ensuring a uniform contribution rate across the training dataset.

Empirical Validation and Practical Significance

Our experimental validation on Perceptron linear classification tasks and Transformer-based LLMing confirms the efficacy of the proposed theory. The highlight of our findings is the showcased ability of the near-optimal learning policy to significantly reduce the requisite training steps for LMs, achieving an impressive speedup compared to conventional methods. This underscores the practical viability and the far-reaching implications of the theory for accelerating LLM training, potentially democratizing access to powerful LMs across research and industry spheres.

Theoretical Implications and Future Insights

The exploration opens numerous avenues for further research, especially in designing efficient strategies for identifying optimal learning policies grounded in our theory. While our empirical studies confirm the theory's validity on smaller scales, there remains a promising challenge to extend these results to LLMs. The convergence of our theory with practical, scalable methods to optimize LM learning policies could revolutionize the training of LLMs, reducing computational costs and making high-performing models accessible to a broader audience.

Conclusion

In conclusion, our work presents a novel theory for the optimal learning of LMs, emphasizing a compression-maximization objective. The Learning Law derived from this theory, supported by empirical evidence, provides a profound insight into the dynamics of optimal learning. This paves the way for future research on practical methods to harness the theory for large-scale LLM training, potentially altering the computational landscape and accessibility of LLMs. The theory's promise for significant learning acceleration highlights its importance and timeliness in the quest for efficient and powerful LLMs.

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