Agent models: Internalizing Chain-of-Action Generation into Reasoning
  models
Authors: name: Yuxiang Zhang, name: Yuqi Yang, name: Jiangming Shu, name: Xinyan Wen, name: Jitao Sang

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