DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents Article Swipe
Taiyi Wang
,
Zhihao Wu
,
Jianheng Liu
,
Jianye Hao
,
Jun Wang
,
Kun Shao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.14803
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.14803
On-device control agents, especially on mobile devices, are responsible for operating mobile devices to fulfill users' requests, enabling seamless and intuitive interactions. Integrating Multimodal Large Language Models (MLLMs) into these agents enhances their ability to understand and execute complex commands, thereby improving user experience. However, fine-tuning MLLMs for on-device control presents significant challenges due to limited data availability and inefficient online training processes. This paper introduces DistRL, a novel framework designed to enhance the efficiency of online RL fine-tuning for mobile device control agents. DistRL employs centralized training and decentralized data acquisition to ensure efficient fine-tuning in the context of dynamic online interactions. Additionally, the framework is backed by our tailor-made RL algorithm, which effectively balances exploration with the prioritized utilization of collected data to ensure stable and robust training. Our experiments show that, on average, DistRL delivers a 3X improvement in training efficiency and enables training data collection 2.4X faster than the leading synchronous multi-machine methods. Notably, after training, DistRL achieves a 20% relative improvement in success rate compared to state-of-the-art methods on general Android tasks from an open benchmark, significantly outperforming existing approaches while maintaining the same training time. These results validate DistRL as a scalable and efficient solution, offering substantial improvements in both training efficiency and agent performance for real-world, in-the-wild device control tasks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.14803
- https://arxiv.org/pdf/2410.14803
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404390499
All OpenAlex metadata
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https://openalex.org/W4404390499Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.14803Digital Object Identifier
- Title
-
DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control AgentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-18Full publication date if available
- Authors
-
Taiyi Wang, Zhihao Wu, Jianheng Liu, Jianye Hao, Jun Wang, Kun ShaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.14803Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.14803Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2410.14803Direct OA link when available
- Concepts
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Reinforcement learning, Asynchronous communication, Computer science, Control (management), Reinforcement, Distributed computing, Artificial intelligence, Computer network, Psychology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.experiments | 131 |
| abstract_inverted_index.exploration | 116 |
| abstract_inverted_index.fine-tuning | 45, 78, 95 |
| abstract_inverted_index.improvement | 140, 165 |
| abstract_inverted_index.in-the-wild | 213 |
| abstract_inverted_index.inefficient | 59 |
| abstract_inverted_index.maintaining | 186 |
| abstract_inverted_index.performance | 210 |
| abstract_inverted_index.prioritized | 119 |
| abstract_inverted_index.real-world, | 212 |
| abstract_inverted_index.responsible | 8 |
| abstract_inverted_index.significant | 51 |
| abstract_inverted_index.substantial | 202 |
| abstract_inverted_index.synchronous | 154 |
| abstract_inverted_index.tailor-made | 110 |
| abstract_inverted_index.utilization | 120 |
| abstract_inverted_index.availability | 57 |
| abstract_inverted_index.improvements | 203 |
| abstract_inverted_index.Additionally, | 103 |
| abstract_inverted_index.decentralized | 89 |
| abstract_inverted_index.interactions. | 21, 102 |
| abstract_inverted_index.multi-machine | 155 |
| abstract_inverted_index.outperforming | 182 |
| abstract_inverted_index.significantly | 181 |
| abstract_inverted_index.state-of-the-art | 171 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |