arXiv:2605.24667v1 Announce Type: cross Abstract: Mean cross-entropy is the standard validation metric for language models, but it can fail to track model quality during training. We examine this in two common scenarios. First, in Qwen2.5-1.5B SFT on synthetic fact-learning, we find that mean CE rises substantially after the initial learning phase while held-out fact-recall accuracy remains near its peak. Second, we find that in top-K distillation on TinyStories, decreasing K improves median CE while worsening mean CE; the Top-5 student attains the highest LLM-judge score and crosses below its
The rapid development and deployment of large language models are highlighting the limitations of current evaluation metrics, necessitating research into more robust indicators of model quality.
Improved metrics for tracking language model quality will lead to more efficient and effective training, validation, and deployment of AI systems, impacting development cycles and performance.
The standard approach to evaluating language models during training may evolve, with median cross-entropy potentially becoming a more reliable indicator than traditional mean cross-entropy.
- · AI researchers
- · LLM developers
- · AI infrastructure providers
- · Inefficient LLM validation processes
Researchers will begin incorporating median CE into their training and validation pipelines for language models.
More accurate model evaluation could accelerate the development of higher-quality and more robust AI applications.
The shift in evaluation metrics may influence future architectural decisions and training methodologies for large language models.
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Read at arXiv cs.LG