Research2h ago
Evaluation Bottlenecks Slow AI Development
High-quality evaluation data now limits model progress more than raw compute. Hugging Face argues that static benchmarks fail as models hit ceiling scores. This creates a feedback loop where developers struggle to measure marginal gains. Researchers must pivot toward dynamic, human-in-the-loop testing to avoid stagnation in model performance.