πŸ“„
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training paper by Jihao Gu
⭐ 38.4
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Paper

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training

by Jihao Gu arxiv/2506.20332
Free2AITools Nexus Index
38.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 80
Q: Quality 60
Tech Context
Vital Performance —

Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like Group Relative Policy Optimization (GRPO). However, existing approaches centers on offline training or local action-level rewards often trap agents in local optima, hindering effective exploration and error correction with the environment. Crucially, we find that directly applying task-level rewards often leads to co...

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Registry ID 2506.20332
License arXiv
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BibTeX 
@misc{arxiv_2506_20332,
  author = {Jihao Gu},
  title = {Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2506.20332}},
  note = {Accessed via Free2AITools.}
}
APA Style
Jihao Gu. (2026). Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training [Paper]. Free2AITools. https://arxiv.org/abs/2506.20332

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 0
Recency (R) 80
Quality (Q) 60

πŸ’¬ Index Insight

FNI V2.0 for Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training: Authority (A:0), Popularity (P:0), Recency (R:80), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like Group Relative Policy Optimization (GRPO). However, existing approaches centers on offline training or local action-level rewards often trap agents in local optima, hindering effective exploration and error correction with the environment. Crucially, we find that directly applying task-level rewards often leads to co..."

❝ Cite Node 

@article{Gu2026Mobile-R1:,
  title={Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training},
  author={Jihao Gu},
  journal={arXiv preprint arXiv:2506.20332},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Jihao Gu

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

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πŸ“Š Research Signals

πŸ“…1970Published
⏱️80RecencyFNI pillar
βœ…60QualityFNI pillar
πŸ—‚οΈcs.AIField

🏷️ Research Topics

instruction tuningvision modelslora finetuning
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πŸ†” Identity & Source

id
2506.20332
slug
2506.20332
source
arxiv
author
Jihao Gu
license
arXiv
tags
arxiv:cs.AI

βš™οΈ Technical Specs

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params billions
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