We introduceSwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks.SwiftSageintegrates the strengths of behavior cloning and prompting large language models (LLMs)  to enhance task completion performance. The framework comprises two primary modules: theSwiftmodule, representing fast and intuitive thinking, and theSagemodule, emulating deliberate thought processes. TheSwiftmodule is a small encoder-decoder LM fine-tuned on the oracle agent’s action trajectories, while theSagemodule employs LLMs such as GPT-4 for subgoal planning and grounding. We develop a heuristic method to harmoniously integrate the two modules, resulting in a more efficient and robust problem-solving process. In 30 tasks from the ScienceWorld benchmark,SwiftSagesignificantly outperforms other methods such as SayCan, ReAct, and Reflexion, demonstrating its effectiveness in solving complex interactive tasks.111Contact:yuchenl@allenai.org