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Former ByteDance Seed Engineer: ByteDance’s iteration cycle reportedly takes half a year; Google only needs three months.

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ME News, April 24 (UTC+8), according to Beating monitoring, Zhang Chi, a former engineer of the ByteDance Seed team and now an assistant professor at Peking University, revealed in the podcast "Into Asia" that ByteDance takes about half a year to complete a round of large model training (pre-training plus post-training), while Google is rumored to need only three months. He believes that iteration speed is one of the core reasons Chinese companies struggle to catch up. Zhang was at ByteDance for about a year, and his math team was more research-oriented; he stated that the group's positioning was "more for publicity," differing from the pre-training and post-training teams responsible for model delivery. Zhang described the benchmaxxing (score-hunting) culture within Seed: team leaders evaluate performance based on the benchmarks they are responsible for, and everyone is chasing higher scores, but "this cannot translate into a good experience in actual use." He said that on paper, Chinese big companies' models can all match the frontier models from the US, but in practice, they are "not good enough." The goal of Seed is to be top global, "but unfortunately, I don't think we have caught up," and even the goal of being number one domestically "has not been achieved." At the end of 2024, Seed believed it had caught up with GPT-4o, but then DeepSeek was released, and the team realized the gap remained; when he joined, the whole group was urgently shifting to reinforcement learning. (Source: BlockBeats)
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