This page organizes papers related to artificial intelligence published around the world. This page is summarized using Google Gemini and is operated on a non-profit basis. The copyright of the paper belongs to the author and the relevant institution. When sharing, simply cite the source.
Jiashun Liu, Johan Obando-Ceron, Han Lu, Yancheng He, Weixun Wang, Wenbo Su, Bo Zheng, Pablo Samuel Castro, Aaron Courville, Ling Pan
Outline
This paper introduces Asymmetric Proximal Policy Optimization (AsyPPO), a simple and scalable framework that restores the critic role while maintaining efficiency in Reinforcement Learning (RL) research for Large Language Models (LLM). AsyPPO leverages lightweight mini-critics to improve learning stability and performance, outperforming existing robust baselines such as GRPO and PPO.
Takeaways, Limitations
•
Takeaways:
◦
A new architecture is presented that re-emphasizes the role of critic in the LLM environment.
◦
Improve performance while maintaining computational efficiency by utilizing lightweight mini-critics.
◦
Improving policy updates by leveraging inter-critic uncertainty.
◦
Achieves performance that surpasses existing baselines across various benchmarks.
•
Limitations:
◦
Trained with as few as 5,000 samples.
◦
Lack of additional information about specific settings and datasets.
◦
Further research is needed to determine whether the results of this paper can be generalized to other LLM models and tasks.
