Truncated Proximal Policy Optimization
Tiantian Fan, Lingjun Liu, Yu Yue, Jiaze Chen, Chengyi Wang, Qiying Yu, Chi Zhang, Zhiqi Lin, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Bole Ma, Mofan Zhang, Gaohong Liu, Ru Zhang, Haotian Zhou, Cong Xie, Ruidong Zhu, Zhi Zhang, Xin Liu, Mingxuan Wang, Lin Yan
2025-06-19
Summary
This paper talks about Truncated Proximal Policy Optimization (T-PPO), a new method that speeds up the training of large language models, especially those that generate long and complex answers.
What's the problem?
The problem is that traditional training methods like PPO are slow because they have to wait for the entire answer to finish before updating the model, which wastes computing power and time, especially with very long responses.
What's the solution?
The researchers developed T-PPO, which allows the model to start learning from partially completed answers instead of waiting for full responses. It uses a special technique called Extended Generalized Advantage Estimation to safely update the model with these partial answers, and separates how parts of the model are updated to save resources and speed up training without losing accuracy.
Why it matters?
This matters because it makes training large language models much faster and more efficient, helping build better AI systems for tasks that need complex reasoning, while using less computing power and time.
Abstract
T-PPO, an extension of PPO, improves training efficiency for Large Language Models by optimizing policy updates and utilizing hardware resources more effectively.