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Checkpoint Merging via Bayesian Optimization in LLM Pretraining (2403.19390v1)

Published 28 Mar 2024 in cs.CL

Abstract: The rapid proliferation of LLMs such as GPT-4 and Gemini underscores the intense demand for resources during their training processes, posing significant challenges due to substantial computational and environmental costs. To alleviate this issue, we propose checkpoint merging in pretraining LLM. This method utilizes LLM checkpoints with shared training trajectories, and is rooted in an extensive search space exploration for the best merging weight via Bayesian optimization. Through various experiments, we demonstrate that: (1) Our proposed methodology exhibits the capacity to augment pretraining, presenting an opportunity akin to obtaining substantial benefits at minimal cost; (2) Our proposed methodology, despite requiring a given held-out dataset, still demonstrates robust generalization capabilities across diverse domains, a pivotal aspect in pretraining.

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Citations (5)
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Summary

  • The paper introduces a Bayesian optimization approach to merge LLM checkpoints, streamlining pretraining while enhancing model performance.
  • It shows that merging adjacent checkpoints significantly improves accuracy compared to using final or distant checkpoints.
  • Experimental evaluations confirm that the method generalizes across benchmarks and LLM architectures, offering nearly free performance enhancements.

Checkpoint Merging via Bayesian Optimization in LLM Pretraining

Introduction

The surge in development and complexity of LLMs has substantially raised the requirements for computational resources alongside concerns about environmental impacts due to energy consumption. Addressing these challenges, this work introduces a method for merging LLM checkpoints to enhance pretraining efficiency through Bayesian optimization. By selectively combining checkpoints based on their training trajectory, the paper aims to reap the benefits of reduced computational requirements without sacrificing, and potentially even enhancing, model performance. This ambitious approach is methodically explored through pilot experiments and substantiated by a meticulous evaluation across several benchmarks.

Pilot Experiments and Findings

Several pilot experiments frame the core questions: which checkpoints to merge, how many to merge, and the optimal method for merging. The findings from these experiments underline the nuanced nature of checkpoint merging:

  • Adjacent Checkpoints Yield Better Performance: Merging checkpoints from consecutive training stages generally outperforms individual models' accuracies and offers substantial improvements over the final training checkpoints. This suggests merging as a potentially cost-effective avenue for performance gains.
  • Deterioration from Merging Distant Checkpoints: Merging checkpoints far apart in the training schedule tends to diminish performance, aligning closer to the less trained of the two models involved. This highlights the importance of judicious selection in the merging process.
  • Optimal Merging Weights: Exploring merging weights uniformly across the [0, 1] range for pairs of checkpoints showed significant variance in performance, pointing to the critical role of weight allocation in the efficacy of merging strategies.

Methodology for Checkpoint Merging

Based on preliminary insights, a Bayesian optimization framework is proposed to systematically identify optimal merging weights. It explores modeling the checkpoint merging as an optimization problem, leveraging Gaussian Processes for efficiently navigating the merging weight space. This strategy is underpinned by two key components:

  1. Objective Function Configuration: Formulated to evaluate the performance of merged LLMs on a specific dataset, focusing on identifying the optimal weight combinations.
  2. Iterative Optimization Process: Through repeated observations and adjustments based on performance feedback, the method seeks to hone in on the most effective merging strategy.

Experimental Evaluation

The approach is thoroughly assessed across multiple datasets, including C-Eval and CMMLU, using both Baichuan2 and DeepSeek checkpoints to validate generality across different model backbones.

  • Performance Gains: Across a variety of benchmarks, the paper reports consistent performance improvements, highlighting the potential for "nearly free" enhancements to pretraining efficiency. This is particularly notable in scenarios where computational resources are limiting factors.
  • Generalization Across Domains: An intriguing aspect of the investigation is the robust generalization capability of the merged LLMs across different domains. Despite the specificity of datasets used to determine merging weights, the resultant models maintained, and in some cases improved, their performance on entirely new tasks and datasets.

Discussion and Implications

The findings from this research offer a promising avenue for mitigating the computational burden associated with training state-of-the-art LLMs. Moreover, the application of Bayesian Optimization presents a novel approach to navigating the complex parameter space of LLMs, potentially setting a precedent for future explorations in model efficiency. However, the work also acknowledges limitations, including the opaque nature of the merging process and its dependency on resource-intensive evaluations for optimization.

Conclusion and Future Directions

This paper contributes a novel method for checkpoint merging to enhance LLM pretraining, leveraging Bayesian optimization to uncover efficient and effective merging strategies. The approach demonstrates substantial promise in improving model performance and efficiency, paving the path for broader applications and further methodological refinements in the pursuit of more sustainable and effective LLM development strategies. Future work may explore deeper insights into the mechanisms of checkpoint merging, broader applications across LLM architectures, and improved optimization methods that further reduce computational overheads.

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