dailyai.report

Jie Tang argues that parameter counts alone no longer define model quality. He proposes a new post-training scaling law integrating data volume, compute allocation, and deployment conditions. This shift moves beyond Chinchilla assumptions to prioritize inference-time efficiency. Practitioners must now optimize for specific runtime environments rather than chasing raw model size.