title: Agent models: Internalizing Chain-of-Action Generation into Reasoning
    models
summary: Traditional agentic workflows rely on external prompts to manage interactions
  with tools and the environment, which limits the autonomy of reasoning models.
  We position Large Agent Models (LAMs) that internalize the generation of
  Chain-of-Action (CoA), enabling the model to autonomously decide when
  and how to use external tools. Our proposed AutoCoA framework combines
  supervised fine-tuning (SFT) and reinforcement learning (RL), allowing the
  model to seamlessly switch between reasoning and action while efficiently
  managing environment interactions. Main components include step-level action
  triggering, trajectory-level CoA optimization, and an internal world model to
  reduce real-environment interaction costs. Evaluations on open-domain QA tasks
  demonstrate that AutoCoA-trained agent models significantly outperform
  ReAct-based workflows in task completion, especially in tasks that require
  long-term reasoning and multi-step actions. Code and dataset are available at
  https://github.com/ADaM-BJTU/AutoCoA
paper_authors._id: 67cfa71827c7f0b2db19f7c2
paper_authors.name: Yuxiang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:22:40.336Z
paper_authors._id: 645b4a2978730bcc103dfe4d
paper_authors.avatarUrl: /avatars/de544de899897fd0a83506ff287123bc.svg
paper_authors.fullname: Yuxiang Zhang
paper_authors.type: user
paper_authors.user: TokerZ
paper_authors._id: 67cfa71827c7f0b2db19f7c3
paper_authors.name: Yuqi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T11:09:10.879Z
paper_authors._id: 65b5b586d2ae98fd67c76d6b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/RR_mNkUAG4zP902WLgeYv.jpeg
paper_authors.fullname: yuqi yang
paper_authors.type: user
paper_authors.user: tzteyang
paper_authors._id: 67cfa71827c7f0b2db19f7c4
paper_authors.name: Jiangming Shu
paper_authors._id: 67cfa71827c7f0b2db19f7c5
paper_authors.name: Xinyan Wen
paper_authors._id: 67cfa71827c7f0b2db19f7c6
paper_authors.name: Jitao Sang
paper_title: Agent models: Internalizing Chain-of-Action Generation into Reasoning
    models
paper_summary: Traditional agentic workflows rely on external prompts to manage interactions
  with tools and the environment, which limits the autonomy of reasoning models.
  We position Large Agent Models (LAMs) that internalize the generation of
  Chain-of-Action (CoA), enabling the model to autonomously decide when
  and how to use external tools. Our proposed AutoCoA framework combines
  supervised fine-tuning (SFT) and reinforcement learning (RL), allowing the
  model to seamlessly switch between reasoning and action while efficiently
  managing environment interactions. Main components include step-level action
  triggering, trajectory-level CoA optimization, and an internal world model to
  reduce real-environment interaction costs. Evaluations on open-domain QA tasks
  demonstrate that AutoCoA-trained agent models significantly outperform
  ReAct-based workflows in task completion, especially in tasks that require
  long-term reasoning and multi-step actions. Code and dataset are available at
  https://github.com/ADaM-BJTU/AutoCoA
paper_ai_keywords: Large Agent Models (LAMs) Chain-of-Action (CoA) AutoCoA framework supervised fine-tuning (SFT) reinforcement learning (RL) step-level action triggering trajectory-level CoA optimization internal world model