HomeTencent Hunyuan Hy4 Preview Launches With Optimized Task Rounds and Lower Token Use
On September 7, the Tencent Hunyuan and WorkBuddy joint team announced the full upgrade and launch of the Hunyuan Hy4preview model. Since its initial release on WorkBuddy, the model has garnered extensive feedback from users and developers. The joint team has completed the first round of specialized iterations to address the most pressing issues raised.
Addressing known issues in the previous version, such as prolonged reasoning and excessive self-verification during complex tasks, this upgrade implements targeted optimizations. To ensure these optimizations do not compromise task performance, the team employed a dual monitoring mechanism combining Bench metrics and human evaluation. The results demonstrate that the model significantly reduces the number of task rounds and input/output token consumption while maintaining performance levels.
This improvement means that in real-world Agent scenarios, the model will simultaneously enhance response efficiency and reduce calling costs.
The joint team stated that they will continue to iterate rapidly and welcome users to keep experiencing the model and providing feedback.
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On September 7, the Tencent Hunyuan and WorkBuddy joint team announced the full upgrade and launch of the Hunyuan Hy4preview model. Since its initial release on WorkBuddy, the model has garnered extensive feedback from users and developers. The joint team has completed the first round of specialized iterations to address the most pressing issues raised.
Addressing known issues in the previous version, such as prolonged reasoning and excessive self-verification during complex tasks, this upgrade implements targeted optimizations. To ensure these optimizations do not compromise task performance, the team employed a dual monitoring mechanism combining Bench metrics and human evaluation. The results demonstrate that the model significantly reduces the number of task rounds and input/output token consumption while maintaining performance levels.
This improvement means that in real-world Agent scenarios, the model will simultaneously enhance response efficiency and reduce calling costs.
The joint team stated that they will continue to iterate rapidly and welcome users to keep experiencing the model and providing feedback.
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