title: SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex
    Interactive Tasks
summary: 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.
paper_authors._id: 64755ca8e0b188d3cb258575
paper_authors.name: Bill Yuchen Lin
paper_authors.status: claimed_verified
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paper_authors.fullname: Bill Yuchen Lin
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paper_authors.name: Yicheng Fu
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paper_authors.name: Karina Yang
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paper_authors.name: Prithviraj Ammanabrolu
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paper_authors.name: Faeze Brahman
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paper_authors.fullname: fa brah
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paper_authors.user: fabrh
paper_authors._id: 64755ca8e0b188d3cb25857a
paper_authors.name: Shiyu Huang
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paper_authors.fullname: Shiyu Huang
paper_authors.type: user
paper_authors.user: ShiyuHuang
paper_authors._id: 64755ca8e0b188d3cb25857b
paper_authors.name: Chandra Bhagavatula
paper_authors._id: 64755ca8e0b188d3cb25857c
paper_authors.name: Yejin Choi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-10T08:39:17.934Z
paper_authors._id: 64d42729f63b01b7f676b176
paper_authors.avatarUrl: /avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
paper_authors.fullname: Yejin Choi
paper_authors.type: user
paper_authors.user: yejinchoinka
paper_authors._id: 64755ca8e0b188d3cb25857d
paper_authors.name: Xiang Ren
paper_title: SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex
    Interactive Tasks
paper_summary: 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.
paper_ai_keywords: agent framework dual-process theory action planning interactive reasoning tasks behavior cloning prompting large language models (LLMs) Swift module encoder-decoder LM oracle agent Sage module subgoal planning grounding heuristic method ScienceWorld benchmark SayCan ReAct Reflexion