Gate News message, April 24 — Zhang Chi, a former engineer at ByteDance’s Seed team and current assistant professor at Peking University, revealed on the podcast “Into Asia” that ByteDance requires approximately six months to complete one full cycle of large language model training (pretraining plus post-training), while Google reportedly needs only three months. Zhang attributed the speed difference as a core reason why Chinese companies struggle to catch up in AI development.
Zhang described a “benchmarking culture” within Seed, where team leaders are evaluated based on benchmark scores they oversee, and all members focus on boosting numbers. However, he noted this does not translate into better user experience in practice. While Chinese major companies’ models appear competitive with U.S. frontier models on paper, they fall short in actual usage. Seed’s goal is to reach global top-tier performance, but Zhang stated he does not believe the team has achieved this, nor has it met the domestic leadership target.
In late 2024, Seed considered itself on par with GPT-4o, but following DeepSeek’s release, the team recognized the gap remained. When Zhang joined, the entire group was urgently pivoting toward reinforcement learning to address the shortfall.
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