dailyai.report

Parameter count alone no longer defines model capability, according to Jie Tang. He argues that data volume, compute allocation, and deployment conditions now carry equal weight. This shift moves beyond Chinchilla assumptions toward post-training scaling laws. Practitioners must now optimize for inference-time compute rather than simply increasing raw model size to gain performance.