DynaAct: Large Language Model Reasoning with Dynamic Action Spaces

TL;DR

DynaAct introduces a method for automatically constructing compact, effective action spaces in large language models to improve reasoning efficiency and performance across diverse complex problems.

Contribution

The paper presents a novel framework that automatically builds a tailored action space using large language models and submodular optimization, enhancing reasoning in complex tasks.

Findings

Significantly improves performance on six benchmarks.

Maintains efficient inference with minimal latency.

Automatically constructs action spaces without manual design.

Abstract

In modern sequential decision-making systems, the construction of an optimal candidate action space is critical to efficient inference. However, existing approaches either rely on manually defined action spaces that lack scalability or utilize unstructured spaces that render exhaustive search computationally prohibitive. In this paper, we propose a novel framework named \textsc{DynaAct} for automatically constructing a compact action space to enhance sequential reasoning in complex problem-solving scenarios. Our method first estimates a proxy for the complete action space by extracting general sketches observed in a corpus covering diverse complex reasoning problems using large language models. We then formulate a submodular function that jointly evaluates candidate actions based on their utility to the current state and their diversity, and employ a greedy algorithm to select an optimal candidate set. Extensive experiments on six diverse standard benchmarks demonstrate that our approach significantly improves overall performance, while maintaining efficient inference without introducing substantial latency. The implementation is available at https://github.com/zhaoxlpku/DynaAct.