  Recent progress in Large Language Models (LLMs) and language agents has
demonstrated significant promise for various future applications across
multiple disciplines. While traditional approaches to language agents often
rely on fixed, handcrafted designs, our research aims to develop both learnable
and dynamic agents. Our method uses an existing framework that abstracts
language agents as graphs. Within this graph framework, we aim to learn a model
that can generate edges for every given input to the language agent. This
allows us to generate edges that represent the flow of communication within the
graph based on the given input, thereby adjusting the internal communication of
a language agent. We learn to generate these edges using a pretrained LLM that
is fine-tuned with reinforcement learning. This LLM can be fine-tuned on
several datasets simultaneously, and we hypothesize that the model learns to
adapt to these different domains during training, achieving good overall
performance when encountering data from different domains during deployment. We
demonstrate that our approach surpasses the previous static approach by nearly
6% accuracy on a combined dataset of MMLU and CMMLU, and by more than 10% when
trained with a sparsity-inducing loss. It also performs superior in additional
experiments conducted with the MMLU and Mini Crossword Puzzles datasets. The
code is available at https://github.com/lukasVierling/DynamicGPTSwarm.
