Pith. sign in

Uishift: Enhancing vlm-based gui agents through self-supervised reinforcement learning

5 Pith papers cite this work. Polarity classification is still indexing.

5 Pith papers citing it

years

2026 4 2025 1

representative citing papers

Trust Region On-Policy Distillation

cs.LG · 2026-05-31 · unverdicted · novelty 5.0

TrOPD stabilizes on-policy distillation for LLMs with trust-region learning, outlier estimation, and off-policy guidance, outperforming prior OPD methods on reasoning and code benchmarks.

Xiaomi-GUI-0 Technical Report

cs.AI · 2026-06-30 · unverdicted · novelty 4.0 · 2 refs

Xiaomi-GUI-0 reports 72.0% success on RealMobile and 78.9% on AndroidWorld via real-device closed-loop training with multi-source data and three-stage RL pipeline.

citing papers explorer

Showing 5 of 5 citing papers.

  • GUI-AC: Enhancing Continual Learning in GUI Agents cs.CV · 2026-06-09 · conditional · none · ref 30

    Grounding-certainty-guided Adaptive Advantage and Dynamic Clipping improve continual GUI grounding over RFT baselines on ScreenSpot-V1/V2/Pro.

  • Trust Region On-Policy Distillation cs.LG · 2026-05-31 · unverdicted · none · ref 48

    TrOPD stabilizes on-policy distillation for LLMs with trust-region learning, outlier estimation, and off-policy guidance, outperforming prior OPD methods on reasoning and code benchmarks.

  • UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics cs.LG · 2026-02-11 · unverdicted · none · ref 10

    UI-Oceanus shows that continual pre-training on forward dynamics predictions from synthetic GUI exploration improves agent success rates by 7% offline and 16.8% online, with gains scaling by data volume.

  • Xiaomi-GUI-0 Technical Report cs.AI · 2026-06-30 · unverdicted · none · ref 9 · 2 links

    Xiaomi-GUI-0 reports 72.0% success on RealMobile and 78.9% on AndroidWorld via real-device closed-loop training with multi-source data and three-stage RL pipeline.

  • A Survey of Reinforcement Learning for Large Reasoning Models cs.CL · 2025-09-10 · accept · none · ref 155

    A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.