arXiv:2512.06553v2 Announce Type: replace-cross Abstract: We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated by the rapid emergence of numerous new LLM families with distinct architectures and training strategies, evaluated on an increasing number of benchmarks. This heterogeneity makes a single global scaling curve inadequate for capturing how performance varies across families and benchmarks. To address this, we propose a latent variable modeling framework in which each LLM family is associated with a l
The proliferation of diverse LLM architectures and training strategies necessitates more sophisticated analytical frameworks to understand performance and scaling.
This framework offers a critical tool for understanding and predicting LLM capabilities, informing research, investment, and deployment strategies in a rapidly evolving field.
The ability to accurately model scaling laws for heterogeneous LLM families could lead to more efficient resource allocation and clearer performance benchmarks.
- · AI researchers
- · LLM developers
- · Venture capitalists investing in AI
- · Companies relying on simplistic scaling assumptions
Improved understanding of how different LLM architectures scale and perform.
More targeted and efficient development of future large language models.
Accelerated progress in AI capabilities due to optimized resource allocation and clearer performance metrics.
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