title: ATLaS: Agent Tuning via Learning Critical Steps
summary: Large Language Model (LLM) agents have demonstrated remarkable generalization
  capabilities across multi-domain tasks. Existing agent tuning approaches
  typically employ supervised finetuning on entire expert trajectories. However,
  behavior-cloning of full trajectories can introduce expert bias and weaken
  generalization to states not covered by the expert data. Additionally, critical
  steps, such as planning, complex reasoning for intermediate subtasks, and
  strategic decision-making, are essential to success in agent tasks, so learning
  these steps is the key to improving LLM agents. For more effective and
  efficient agent tuning, we propose ATLaS that identifies the critical steps in
  expert trajectories and finetunes LLMs solely on these steps with reduced
  costs. By steering the training's focus to a few critical steps, our method
  mitigates the risk of overfitting entire trajectories and promotes
  generalization across different environments and tasks. In extensive
  experiments, an LLM finetuned on only 30% critical steps selected by ATLaS
  outperforms the LLM finetuned on all steps and recent open-source LLM agents.
  ATLaS maintains and improves base LLM skills as generalist agents interacting
  with diverse environments.
paper_authors._id: 67c7c183203958ca9c09171a
paper_authors.name: Zhixun Chen
paper_authors._id: 67c7c183203958ca9c09171b
paper_authors.name: Ming Li
paper_authors._id: 67c7c183203958ca9c09171c
paper_authors.name: Yuxuan Huang
paper_authors._id: 67c7c183203958ca9c09171d
paper_authors.name: Yali Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:15:28.895Z
paper_authors._id: 646b3c09628e5b50b2dd1174
paper_authors.avatarUrl: /avatars/f96a4d375ea44d10891f4b47b7d7986e.svg
paper_authors.fullname: yali du
paper_authors.type: user
paper_authors.user: YaliDU
paper_authors._id: 67c7c183203958ca9c09171e
paper_authors.name: Meng Fang
paper_authors._id: 67c7c183203958ca9c09171f
paper_authors.name: Tianyi Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-05T10:12:17.603Z
paper_authors._id: 647f5af5b0e96764589f3b2a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
paper_authors.fullname: Tianyi Zhou
paper_authors.type: user
paper_authors.user: zhoutianyi
paper_title: ATLaS: Agent Tuning via Learning Critical Steps
paper_summary: Large Language Model (LLM) agents have demonstrated remarkable generalization
  capabilities across multi-domain tasks. Existing agent tuning approaches
  typically employ supervised finetuning on entire expert trajectories. However,
  behavior-cloning of full trajectories can introduce expert bias and weaken
  generalization to states not covered by the expert data. Additionally, critical
  steps, such as planning, complex reasoning for intermediate subtasks, and
  strategic decision-making, are essential to success in agent tasks, so learning
  these steps is the key to improving LLM agents. For more effective and
  efficient agent tuning, we propose ATLaS that identifies the critical steps in
  expert trajectories and finetunes LLMs solely on these steps with reduced
  costs. By steering the training's focus to a few critical steps, our method
  mitigates the risk of overfitting entire trajectories and promotes
  generalization across different environments and tasks. In extensive
  experiments, an LLM finetuned on only 30% critical steps selected by ATLaS
  outperforms the LLM finetuned on all steps and recent open-source LLM agents.
  ATLaS maintains and improves base LLM skills as generalist agents interacting
  with diverse environments.
paper_ai_summary: ATLaS identifies and finetunes LLM agents on critical steps from expert trajectories, enhancing generalization and performance compared to full trajectory finetuning.
paper_ai_keywords: Large Language Model LLM behavior-cloning expert trajectories generalization planning complex reasoning strategic decision-making ATLaS overfitting