State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study
An end-to-end success rate alone cannot distinguish failure to learn an action from failure to apply it as task state changes. We study this distinction in deterministic tool workflows that provide the current artifact state and candidate action recipes. Using 1.7B, 8B, and 27B Qwen models, we compare supervised learni...
Dong Nie, Rui-Yi Lan, Yi-Xin Nie et al.
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