GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

• URL: http://arxiv.org/abs/2602.22190v1
• Date: Wed, 25 Feb 2026 18:34:57 GMT
• Title: GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL
• Abstract summary: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.<n>This gap stems from a shortage of high-quality, action-aligned reasoning data.<n>We present GUI-Libra, a tailored training recipe that addresses these challenges.
• Score: 64.8155693023222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 demonstrated action is used for verification. This makes offline step-wise metrics weak predictors of online task success. In this work, we present GUI-Libra, a tailored training recipe that addresses these challenges. First, to mitigate the scarcity of action-aligned reasoning data, we introduce a data construction and filtering pipeline and release a curated 81K GUI reasoning dataset. Second, to reconcile reasoning with grounding, we propose action-aware SFT that mixes reasoning-then-action and direct-action data and reweights tokens to emphasize action and grounding. Third, to stabilize RL under partial verifiability, we identify the overlooked importance of KL regularization in RLVR and show that a KL trust region is critical for improving offline-to-online predictability; we further introduce success-adaptive scaling to downweight unreliable negative gradients. Across diverse web and mobile benchmarks, GUI-Libra consistently improves both step-wise accuracy and end-to-end task completion. Our results suggest that carefully designed post-training and data curation can unlock significantly stronger task-solving capabilities without costly online data collection. We release our dataset, code, and models to facilitate further research on data-efficient post-training for reasoning-capable GUI agents.

Related papers

• StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents [67.03593791535786]
  Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, digital environments.<n>To mitigate this issue, recent studies introduce Process Reward Models (PRMs)<n>PRMs provide finer-grained training feedback through global milestone verification or local step-level evaluation.<n>We propose StainFlow, an entity-stain-flow process reward model for GUI Agents.
  arXiv  Detail & Related papers  (2026-06-05T08:17:28Z)
• Don't Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction [4.081183001322422]
  We propose VeriGUI, which explicitly models action outcomes and recovery under noisy environments.<n>A two-stage training pipeline that combines Robust SFT with synthetic failure trajectories and GRPO with asymmetric verification rewards is developed.<n>Experiments show that VeriGUI significantly reduces failure loops and improves recovery success while maintaining standard task performance.
  arXiv  Detail & Related papers  (2026-04-07T06:16:23Z)
• Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards [76.49428173793386]
  LLMs must invoke multiple dependent APIs in the correct order while propagating intermediate outputs.<n>Training models to handle such faces two obstacles: existing environments focus on simple per-turn function calls with simulated data, and binary rewards.<n>We present a framework addressing both challenges. First, we construct a reinforcement learning environment backed by a large-scale cache of real API responses, enabling a data synthesis pipeline that samples valid multi-step orchestration traces.<n>Second, we propose a graduated reward design that decomposes correctness into atomic validity and orchestration.
  arXiv  Detail & Related papers  (2026-03-25T18:31:39Z)
• SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding [41.98672557723593]
  We introduce SWEQA-Pro, a benchmark constructed from diverse, long-tail repositories with executable environments.<n>We also propose a scalable synthetic data pipeline that powers a two-stage training recipe: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning from AI Feedback (RLAIF)<n> Empirically, a Qwen3-8B model trained with our recipe surpasses GPT-4o by 2.3 points on SWE-QA-Pro and substantially narrows the gap to state-of-the-art models.
  arXiv  Detail & Related papers  (2026-03-17T05:12:48Z)
• GAIA: A Data Flywheel System for Training GUI Test-Time Scaling Critic Models [18.14416135619429]
  We propose the GUI Action Critic's Data Flywheel System (GAIA), a training framework that enables the models to have iterative critic capabilities.<n>Specifically, we train an Intuitive Critic Model (ICM) using positive and negative action examples from a base agent.<n>We conduct experiments on various datasets and demonstrate that the proposed ICM can improve the test-time performance of various closed-source and open-source models.
  arXiv  Detail & Related papers  (2026-01-26T06:29:41Z)
• GUI-Shepherd: Reliable Process Reward and Verification for Long-Sequence GUI Tasks [75.50160982584943]
  We introduce a Process Reward Model that provides dense, step-by-step feedback to guide agents.<n>Gui-Shepherd is trained on a diverse large-scale data set of $52$k interactions.<n>We are the first to conduct a systematic study of process supervision in GUI agents.
  arXiv  Detail & Related papers  (2025-09-28T08:35:16Z)
• Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation [83.92224427735859]
  We introduce a pre-operative critic mechanism that provides effective feedback prior to the actual execution.<n>We develop a reasoning-bootstrapping based data collection pipeline to create a GUI-Critic-Train and a GUI-Critic-Test.<n>Our model offers significant advantages in critic accuracy compared to current MLLMs.
  arXiv  Detail & Related papers  (2025-06-05T04:12:36Z)
• ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search [53.40810298627443]
  ReGUIDE is a framework for web grounding that enables MLLMs to learn data efficiently through self-generated reasoning and spatial-aware criticism.<n>Our experiments demonstrate that ReGUIDE significantly advances web grounding performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-05-21T08:36:18Z)
• InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners [41.22438639369124]
  InfiGUI-R1 is an MLLM-based GUI agent developed through our Actor2Reasoner framework.<n>We employ Spatial Reasoning Distillation to transfer cross-modal spatial reasoning capabilities from teacher models to MLLMs.<n>We refine the basic reasoner into a deliberative one using Reinforcement Learning.
  arXiv  Detail & Related papers  (2025-04-19T09:25:55Z)
• Offline Reinforcement Learning from Datasets with Structured Non-Stationarity [50.35634234137108]
  Current Reinforcement Learning (RL) is often limited by the large amount of data needed to learn a successful policy. We address a novel Offline RL problem setting in which, while collecting the dataset, the transition and reward functions gradually change between episodes but stay constant within each episode. We propose a method based on Contrastive Predictive Coding that identifies this non-stationarity in the offline dataset, accounts for it when training a policy, and predicts it during evaluation.
  arXiv  Detail & Related papers  (2024-05-23T02:41:36Z)
• Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks [23.952923773407043]
  Delayed feedback problem is one of the most pressing challenges in predicting conversion rate. We propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM) It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model.
  arXiv  Detail & Related papers  (2023-08-15T23:49:07Z)
• FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations [54.34189781923818]
  FIRE is a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment.<n>We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function.<n>We show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
  arXiv  Detail & Related papers  (2022-09-28T19:49:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.