Extending GUI-Libra to fully online interactive reinforcement learning

Investigate the extension of the GUI-Libra post-training framework for native GUI agents from offline optimization on static datasets to a fully online, interactive reinforcement learning scheme that trains through environment interaction, and systematically characterize the design choices, training stability, and performance implications of such an online extension.

Background

The paper proposes GUI-Libra, a data-efficient post-training framework that combines action-aware supervised fine-tuning and conservative RL on partially verifiable offline datasets, deliberately avoiding costly online environment interaction. Throughout the work, the authors analyze offline-to-online predictability and show strong results without online rollouts.

In the Limitations, the authors explicitly note that their training uses a relatively limited amount of data and does not explore fully online interactive training. They emphasize that fully online RL is expensive and infrastructure-intensive, and explicitly leave a systematic study of extending GUI-Libra to a fully online scheme for future work.