  By formally defining the training processes of large language models (LLMs),
which usually encompasses pre-training, supervised fine-tuning, and
reinforcement learning with human feedback, within a single and unified machine
learning paradigm, we can glean pivotal insights for advancing LLM
technologies. This position paper delineates the parallels between the training
methods of LLMs and the strategies employed for the development of agents in
two-player games, as studied in game theory, reinforcement learning, and
multi-agent systems. We propose a re-conceptualization of LLM learning
processes in terms of agent learning in language-based games. This framework
unveils innovative perspectives on the successes and challenges in LLM
development, offering a fresh understanding of addressing alignment issues
among other strategic considerations. Furthermore, our two-player game approach
sheds light on novel data preparation and machine learning techniques for
training LLMs.
