Cut Cross-Entropy (CCE) solves a critical bottleneck in training large language models by optimizing the memory-intensive cross-entropy loss computation.

• Eliminates the need to store the full logit matrix, which traditionally consumes an order of magnitude more memory than the rest of the LLM
• Achieves substantial memory reduction while maintaining the same numerical output as standard cross-entropy
• Enables more efficient training of LLMs with large vocabularies
• Represents a significant engineering breakthrough in computational efficiency for AI systems

This innovation allows researchers to train larger models on existing hardware or reduce costs by using less expensive infrastructure, potentially accelerating AI development across the industry.

Cut Your Losses in Large-Vocabulary Language Models