DynaAct: Large Language Model Reasoning with Dynamic Action Spaces
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 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.
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Xueliang Zhao
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2025-11-13T13:06:11.091Z
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Xueliang Zhao
xl-zhao
user
xl-zhao
6914b626a1b06ca3cc8133c0
Wei Wu
6914b626a1b06ca3cc8133c1
Jian Guan
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2025-12-17T10:09:52.543Z
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Jian Guan
Jiann
user
Jiann
6914b626a1b06ca3cc8133c2
Qintong Li
6914b626a1b06ca3cc8133c3
Lingpeng Kong
DynaAct: Large Language Model Reasoning with Dynamic Action Spaces
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 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.
A new framework, DynaAct, automatically constructs a compact action space using large language models and submodular functions to enhance sequential reasoning in complex problem-solving scenarios, improving performance while maintaining efficiency.
sequential decision-making systems optimal candidate action space large language models submodular function greedy algorithm sequential reasoning