DynaAct: Large Language Model Reasoning with Dynamic Action Spaces

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM

The introduction of DynaAct marks a significant advancement in the field of AI, particularly in sequential decision-making systems. Traditional methods often rely on manually defined action spaces or unstructured spaces that hinder scalability and efficiency. DynaAct addresses these challenges by automatically estimating a proxy for the complete action space through large language models, which allows for a more compact and manageable action space. By formulating a submodular function to evaluate candidate actions based on their utility and diversity, and employing a greedy algorithm for optimal selection, DynaAct demonstrates substantial improvements in performance across six diverse standard benchmarks. The ability to maintain efficient inference without introducing significant latency is particularly noteworthy, as it enhances the practicality of deploying such systems in real-world applications. This framework not only contributes to the academic discourse but also has implications…

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