ReCode: Unify Plan and Action for Universal Granularity Control

TL;DR

ReCode introduces a unified recursive code generation paradigm that enables large language models to seamlessly operate across different decision granularities, improving adaptability and efficiency in hierarchical decision-making tasks.

Contribution

It proposes a novel unified framework that combines planning and action in a recursive code structure, allowing dynamic control over decision granularity in LLM-based agents.

Findings

Outperforms advanced baselines in inference tasks

Demonstrates high data efficiency in training

Enables flexible hierarchical decision-making

Abstract

Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitation stems from existing paradigms that enforce a rigid separation between high-level planning and low-level action, which impairs dynamic adaptability and limits generalization. We propose ReCode (Recursive Code Generation), a novel paradigm that addresses this limitation by unifying planning and action within a single code representation. In this representation, ReCode treats high-level plans as abstract placeholder functions, which the agent then recursively decomposes into finer-grained sub-functions until reaching primitive actions. This recursive approach dissolves the rigid boundary between plan and action, enabling the agent to dynamically control its decision granularity. Furthermore, the recursive structure inherently generates rich, multi-granularity training data, enabling models to learn hierarchical decision-making processes. Extensive experiments show ReCode significantly surpasses advanced baselines in inference performance and demonstrates exceptional data efficiency in training, validating our core insight that unifying planning and action through recursive code generation is a powerful and effective approach to achieving universal granularity control. The code is available at https://github.com/FoundationAgents/ReCode.