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m-ricย 
posted an update 5 days ago
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๐—”๐—ป๐—ฑ๐—ฟ๐—ผ๐—ถ๐—ฑ๐—Ÿ๐—ฎ๐—ฏ: ๐—™๐—ถ๐—ฟ๐˜€๐˜ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ๐—ฎ๐˜๐—ถ๐—ฐ ๐—ฏ๐—ฒ๐—ป๐—ฐ๐—ต๐—บ๐—ฎ๐—ฟ๐—ธ ๐—ณ๐—ผ๐—ฟ ๐—”๐—ป๐—ฑ๐—ฟ๐—ผ๐—ถ๐—ฑ ๐—บ๐—ผ๐—ฏ๐—ถ๐—น๐—ฒ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐˜€๐—ต๐—ผ๐˜„๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐˜€๐—บ๐—ฎ๐—น๐—น, ๐—ณ๐—ถ๐—ป๐—ฒ-๐˜๐˜‚๐—ป๐—ฒ๐—ฑ ๐—ผ๐—ฝ๐—ฒ๐—ป ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐—ฐ๐—ฎ๐—ป ๐—ฝ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—ฎ ๐—๐—”๐—ฅ๐—ฉ๐—œ๐—ฆ ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ ๐—ผ๐—ป ๐˜†๐—ผ๐˜‚๐—ฟ ๐˜€๐—บ๐—ฎ๐—ฟ๐˜๐—ฝ๐—ต๐—ผ๐—ป๐—ฒ ๐Ÿ“ฑ๐Ÿ”ฅ

A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.

They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.

The team built:

๐Ÿ“Šย A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically

๐Ÿ“๐Ÿ“ฑย A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces

โœ…ย An instruction dataset of 10.5k operation traces for training mobile agents

Key insights:

- ๐Ÿ“ˆ Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although itโ€™s much smaller
- โš™๏ธ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.

Read their paper here ๐Ÿ‘‰ AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2410.24024)
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