At the June 5 Tencent Cloud AI Industry Application Conference, a dialog between CSIG CEO Tang Daosheng and Yao Shunyu — who joined Tencent half a year ago as chief AI scientist — publicly revealed the complete training methodology behind Hunyuan 3 Preview: opposite to the mainstream "scale parameters, scale benchmark" path, Tencent is taking a "cut data, simplify" product-driven route.
Data view: The first thing Yao Shunyu did after joining was not to pile up tokens, but to cut data. He pushed to identify and remove "data that looks like it can pile up volume but actually doesn't help training, or is even harmful," bringing "data quality" back to the core of model training. Tang Daosheng's evaluation: "If you don't understand the importance of data quality and just blindly chase more T of tokens, you can't make the decision to cut data."
Architecture view: Following the scaling-law line of thought, Hunyuan 3 chose to simplify the architecture — removing unnecessary tricks, making the architecture "simpler," letting scaling truly scale. The result is "although today's model isn't that large, the improvement compared to before is already huge."
Product co-design: The Hunyuan team and the Yuanbao team have now moved to the same building. 80% of Yuanbao users have switched to Hy3 Preview, including the latest AI speech recognition, dialect recognition, all trained on the Hy3 Preview base. Hunyuan 3 Preview's token call volume is twice that of 2.0, and retention has also risen significantly.
This combination reflects a shift: with tight compute and persistently high token costs, "tuning product experience" converts to sustainable business value more than "topping the leaderboard." It's a sober reminder to latecomers: in model training, "less is more" — the premise is really understanding the product, daring to cut data.