ATLaS: Agent Tuning via Learning Critical Steps
This addresses the issue of expert bias and weak generalization in multi-domain agent tasks for AI researchers and practitioners, representing an incremental improvement over existing tuning methods.
The paper tackled the problem of agent tuning for LLM agents by proposing ATLaS, which identifies and finetunes on critical steps in expert trajectories, resulting in an LLM finetuned on only 30% critical steps outperforming models trained on all steps and recent open-source agents.
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.