arXiv:2502.11034v3 Announce Type: replace Abstract: Loss spikes remain a persistent obstacle in large-scale language model pretraining. While previous research has attempted to identify the root cause of loss spikes by investigating individual factors, we observe that, in practice, such spikes are typically triggered by the confluence of heterogeneous factors. Empirically, loss spikes may arise from a combination of data outliers, hardware or transient computational faults, numerical precision issues, and hyperparameter settings. Regardless of the underlying cause, these spikes manifest as uns
The continuous scaling of LLMs makes pretraining stability a critical and ongoing challenge, requiring constant innovation in optimization techniques to manage complexity and efficiency during development.
Improved stability in LLM pretraining directly translates to more reliable and efficient development of large AI models, potentially reducing computational costs and accelerating AI progress for all developers.
This research provides a more robust method, Adaptive Gradient Clipping (AdaGC), to mitigate 'loss spikes' during LLM pretraining, moving beyond single-factor analyses to address heterogeneous causes of instability.
- · AI model developers
- · Cloud compute providers
- · AI research institutions
- · Large Language Models
- · Inefficient LLM training methodologies
More stable and faster training of large language models becomes possible through enhanced gradient clipping techniques.
Reduced compute costs and improved model quality could lead to a faster pace of innovation and deployment of advanced AI applications.
The widespread adoption of more stable training methods facilitates even larger and more complex AI models, potentially accelerating the development of generally capable AI agents.
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Read at arXiv cs.LG