Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos Georgios Papoudakis Matthieu Zimmer Thomas Coste Zhihao Wu Jingxuan Chen Khyati Khandelwal James Doran Xidong Feng Jiacheng Liu Zheng Xiong Yicheng Luo Jianye Hao Kun Shao Haitham Bou-Ammar Jun Wang
A key method for creating Artificial Intelligence (AI) agents is
Reinforcement Learning (RL). However, constructing a standalone RL policy that
maps perception to action directly encounters severe problems, chief among them
being its lack of generality across multiple tasks and the need for a large
amount of training data. The leading cause is that it cannot effectively
integrate prior information into the perception-action cycle when devising the
policy. Large language models (LLMs) emerged as a fundamental way to
incorporate cross-domain knowledge into AI agents but lack crucial learning and
adaptation toward specific decision problems. This paper presents a general
framework model for integrating and learning structured reasoning into AI
agents' policies. Our methodology is motivated by the modularity found in the
human brain. The framework utilises the construction of intrinsic and extrinsic
functions to add previous understandings of reasoning structures. It also
provides the adaptive ability to learn models inside every module or function,
consistent with the modular structure of cognitive processes. We describe the
framework in-depth and compare it with other AI pipelines and existing
frameworks. The paper explores practical applications, covering experiments
that show the effectiveness of our method. Our results indicate that AI agents
perform and adapt far better when organised reasoning and prior knowledge are
embedded. This opens the door to more resilient and general AI agent systems.