GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL
arXiv:2602.22190v2 Announce Type: replace Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a shortage of high-quality, action-aligned reasoning data, and the direct adoption of generic post-training pipelines that overlook the unique challenges of GUI agents. We identify two fundamental issues in these pipelines: (i) standard SFT with CoT reasoning often hurts grounding, and (ii) step-wise RLVR-tyle training faces partial verifiability, where multiple actions can be correct but only a single de
The paper addresses critical limitations in open-source GUI agents, a current frontier in AI research, by proposing new training methodologies that improve their reasoning and action capabilities.
Improving GUI agents is crucial for expanding AI's practical applications beyond text-based interactions, enabling more complex automation across diverse software environments.
New training paradigms leveraging action-aware supervision and partially verifiable reinforcement learning could lead to significantly more capable and widely deployed AI agents for software interaction.
- · AI Agent Developers
- · Open-source AI Community
- · Software Automation Industry
- · Closed-source AI Systems (eventually, if open-source catches up quickly)
- · Manual software process workers
Open-source AI agents become more proficient at complex software navigation and task completion.
Increased adoption of AI agents for automating professional and personal digital tasks, leading to efficiency gains across industries.
The development of truly general-purpose AI agents capable of operating across all digital interfaces, potentially reshaping human-computer interaction and workforce roles.
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Read at arXiv cs.LG