Agent models: Internalizing Chain-of-Action Generation into Reasoning
  models
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
67cfa71827c7f0b2db19f7c2
Yuxiang Zhang
claimed_verified
2025-03-11T08:22:40.336Z
645b4a2978730bcc103dfe4d
/avatars/de544de899897fd0a83506ff287123bc.svg
Yuxiang Zhang
user
TokerZ
67cfa71827c7f0b2db19f7c3
Yuqi Yang
admin_assigned
2025-03-11T11:09:10.879Z
65b5b586d2ae98fd67c76d6b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/RR_mNkUAG4zP902WLgeYv.jpeg
yuqi yang
user
tzteyang
67cfa71827c7f0b2db19f7c4
Jiangming Shu
67cfa71827c7f0b2db19f7c5
Xinyan Wen
67cfa71827c7f0b2db19f7c6
Jitao Sang
Agent models: Internalizing Chain-of-Action Generation into Reasoning
  models
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
The AutoCoA framework combines supervised fine-tuning and reinforcement learning to enhance Large Agent Models, enabling autonomous decision-making and efficient multitask handling in open-domain QA tasks.
Large Agent Models (LAMs) Chain-of-Action (CoA) AutoCoA supervised fine-tuning (SFT) reinforcement learning (RL) step-level action triggering trajectory-level CoA optimization internal world model open-domain QA tasks ReAct-based workflows