SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex
  Interactive Tasks
We introduce SwiftSage, a novel agent framework inspired by the dual-process
theory of human cognition, designed to excel in action planning for complex
interactive reasoning tasks. SwiftSage integrates the strengths of behavior
cloning and prompting large language models (LLMs) to enhance task completion
performance. The framework comprises two primary modules: the Swift module,
representing fast and intuitive thinking, and the Sage module, emulating
deliberate thought processes. The Swift module is a small encoder-decoder LM
fine-tuned on the oracle agent's action trajectories, while the Sage module
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, SwiftSage significantly outperforms other methods such as SayCan,
ReAct, and Reflexion, demonstrating its effectiveness in solving complex
real-world tasks.
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Bill Yuchen Lin
claimed_verified
2023-05-31T07:24:41.704Z
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Bill Yuchen Lin
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yuchenlin
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Yicheng Fu
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Karina Yang
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Prithviraj Ammanabrolu
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Faeze Brahman
claimed_verified
2023-05-30T18:00:14.830Z
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fa brah
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Shiyu Huang
admin_assigned
2023-05-30T14:55:59.166Z
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Shiyu Huang
user
ShiyuHuang
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Chandra Bhagavatula
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Yejin Choi
admin_assigned
2023-08-10T08:39:17.934Z
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Yejin Choi
user
yejinchoinka
64755ca8e0b188d3cb25857d
Xiang Ren
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex
  Interactive Tasks
We introduce SwiftSage, a novel agent framework inspired by the dual-process
theory of human cognition, designed to excel in action planning for complex
interactive reasoning tasks. SwiftSage integrates the strengths of behavior
cloning and prompting large language models (LLMs) to enhance task completion
performance. The framework comprises two primary modules: the Swift module,
representing fast and intuitive thinking, and the Sage module, emulating
deliberate thought processes. The Swift module is a small encoder-decoder LM
fine-tuned on the oracle agent's action trajectories, while the Sage module
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, SwiftSage significantly outperforms other methods such as SayCan,
ReAct, and Reflexion, demonstrating its effectiveness in solving complex
real-world tasks.
SwiftSage, a dual-module agent framework combining behavior cloning and large language models, excels at complex interactive reasoning tasks by integrating intuitive and deliberate thinking.
agent framework dual-process theory behavior cloning large language models encoder-decoder LM subgoal planning grounding heuristic method ScienceWorld benchmark SayCan ReAct Reflexion