ATLaS: Agent Tuning via Learning Critical Steps
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.
67c7c183203958ca9c09171a
Zhixun Chen
67c7c183203958ca9c09171b
Ming Li
67c7c183203958ca9c09171c
Yuxuan Huang
67c7c183203958ca9c09171d
Yali Du
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2025-03-05T11:15:28.895Z
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yali du
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YaliDU
67c7c183203958ca9c09171e
Meng Fang
67c7c183203958ca9c09171f
Tianyi Zhou
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2025-03-05T10:12:17.603Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
ATLaS: Agent Tuning via Learning Critical Steps
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.
ATLaS identifies and finetunes LLM agents on critical steps from expert trajectories, enhancing generalization and performance compared to full trajectory finetuning.
Large Language Model LLM behavior-cloning expert trajectories generalization planning complex reasoning strategic decision-making ATLaS overfitting