Core Insights - The article discusses the limitations of current AI agent frameworks, particularly the fixed decision granularity that restricts adaptability and planning capabilities [2][3] - It introduces ReCode (Recursive Code Generation), a new paradigm that unifies planning and execution, allowing agents to switch between different granularities seamlessly [3][11] Current AI Agent Limitations - Existing frameworks like ReAct operate on a fixed, fine-grained observation-action loop, which can lead to inefficiencies in complex tasks [9] - Agents with planners separate planning and execution, which hampers dynamic adaptability and learning from execution feedback [10] ReCode Framework - ReCode proposes a unified code representation for all decisions, regardless of granularity, allowing for recursive breakdown of high-level plans into executable actions [12][14] - The workflow involves converting task instructions into a root placeholder function, which is then expanded recursively into specific actions [15][16] Performance Improvements - Experimental results show that ReCode outperforms ReAct, achieving an average performance increase from 47.4% to 60.8% across three environments [6][20] - ReCode also reduces reasoning costs by 79% and training sample requirements to 27% of what ReAct needs [6][23] Cost Efficiency - The average cost of a ReCode trajectory is 78.9% lower than ReAct, demonstrating significant cost advantages due to structured exploration [23][24] Training Efficiency - In the ScienceWorld environment, ReCode achieves 88.5% reward with only 3,500 training samples, compared to 12,833 samples required by ReAct [25] - ReCode's recursive structure generates hierarchical training data, enhancing learning efficiency [27] Future Directions - Future research may focus on enhancing the model's ability to understand recursive decomposition logic and optimizing planning strategies through learning [27]