AI model benchmarks for early development and external reporting

An attendee’s recap of a summit chat with Marin project lead David Hall says model-card evaluations are just one kind of benchmark: post-training results used for external communication. It says internal evaluations should work across model scales and give signals weeks or months before traditional benchmarks yield nonzero scores. The recap says pretraining mostly uses measures such as loss and perplexity, and calls for simpler frontier-task benchmarks that offer earlier signals for smaller models.