Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. Pythia: A suite for analyzing large language models across training and scaling.
  2. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.
  3. Checkpoint ensembles: Ensemble methods from a single training process. arXiv preprint arXiv:1710.03282.
  4. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1–113.
  5. Training verifiers to solve math word problems.
  6. Deepseek llm: Scaling open-source language models with longtermism.
  7. Llmcarbon: Modeling the end-to-end carbon footprint of large language models. arXiv preprint arXiv:2309.14393.
  8. Peter I. Frazier. 2018. A tutorial on bayesian optimization.
  9. Clive WJ Granger. 1989. Invited review combining forecasts—twenty years later. Journal of forecasting, 8(3):167–173.
  10. Olmo: Accelerating the science of language models.
  11. Measuring massive multitask language understanding.
  12. Portfolio allocation for bayesian optimization. In UAI, pages 327–336.
  13. Token dropping for efficient BERT pretraining. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3774–3784, Dublin, Ireland. Association for Computational Linguistics.
  14. C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models. In Advances in Neural Information Processing Systems.
  15. Dataless knowledge fusion by merging weights of language models. arXiv preprint arXiv:2212.09849.
  16. Population parameter averaging (papa). arXiv preprint arXiv:2304.03094.
  17. Efficient global optimization of expensive black-box functions. Journal of Global optimization, 13:455–492.
  18. Solar 10.7 b: Scaling large language models with simple yet effective depth up-scaling. arXiv preprint arXiv:2312.15166.
  19. Cmmlu: Measuring massive multitask language understanding in chinese.
  20. Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118.
  21. Hanayo: Harnessing wave-like pipeline parallelism for enhanced large model training efficiency. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–13.
  22. Michael S Matena and Colin A Raffel. 2022. Merging models with fisher-weighted averaging. Advances in Neural Information Processing Systems, 35:17703–17716.
  23. Jonas Močkus. 1975. On bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference: Novosibirsk, July 1–7, 1974, pages 400–404. Springer.
  24. Gpt-4 technical report.
  25. R OpenAI. 2023. Gpt-4 technical report. arxiv 2303.08774. View in Article, 2:13.
  26. Elle: Efficient lifelong pre-training for emerging data. arXiv preprint arXiv:2203.06311.
  27. Zero: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–16. IEEE.
  28. Warm: On the benefits of weight averaged reward models.
  29. Sebastian Ruder and Barbara Plank. 2017. Learning to select data for transfer learning with bayesian optimization. arXiv preprint arXiv:1707.05246.
  30. Early weight averaging meets high learning rates for llm pre-training.
  31. Matthias Seeger. 2004. Gaussian processes for machine learning. International journal of neural systems, 14(02):69–106.
  32. Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053.
  33. Interactive text ranking with bayesian optimization: A case study on community qa and summarization. Transactions of the Association for Computational Linguistics, 8:759–775.
  34. Sidak Pal Singh and Martin Jaggi. 2023. Model fusion via optimal transport.
  35. Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995.
  36. Energy and policy considerations for deep learning in nlp. arXiv preprint arXiv:1906.02243.
  37. Gemini: A family of highly capable multimodal models.
  38. Llama: Open and efficient foundation language models.
  39. Joachim Utans. 1996. Weight averaging for neural networks and local resampling schemes. In Proc. AAAI-96 Workshop on Integrating Multiple Learned Models. AAAI Press, pages 133–138. Citeseer.
  40. Knowledge fusion of large language models.
  41. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International Conference on Machine Learning, pages 23965–23998. PMLR.
  42. Baichuan 2: Open large-scale language models.
  43. Bayesian optimization of text representations. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2100–2105, Lisbon, Portugal. Association for Computational Linguistics.
  44. Language models are super mario: Absorbing abilities from homologous models as a free lunch.
Citations (5)
View on Semantic Scholar

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.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets