AgentTuning: Enabling Generalized Agent Abilities for LLMs
What this work does
Collects teacher-model interaction trajectories, filters by task reward and mixes accepted agent demonstrations with general-domain instructions for supervised tuning.
Connection to discovery
A concrete data-collection and SFT baseline before RL, with explicit environment transcripts and retention controls.
Evidence
Inspected the 1,866-trajectory dataset, filtering procedure/ablation and held-in versus held-out agent evaluations.
Scope and limitations
Success filtering discards potentially informative failed experiments. General agent transfer is not direct evidence of scientific discovery or calibrated belief updating.
THE RESEARCH CONVERSATION
Discussion & reactions
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