Beyond Syntax: Action Semantics Learning for App Agents
📰 ArXiv cs.AI
Fine-tuning smaller open-source LLMs for App agents can reduce compute costs and API dependency, but requires action semantics learning beyond syntax
Action Steps
- Fine-tune smaller open-source LLMs to reduce compute costs and API dependency
- Use action semantics learning to go beyond syntax and improve App agent performance
- Evaluate the effectiveness of the fine-tuned model in interpreting user intent and operating smartphone Apps
Who Needs to Know This
AI engineers and researchers working on App agents can benefit from this approach to improve the efficiency and effectiveness of their models, while product managers can consider the potential cost savings and increased autonomy
Key Insight
💡 Fine-tuning smaller open-source LLMs with action semantics learning can improve the efficiency and effectiveness of App agents
Share This
📱 Fine-tune open-source LLMs for App agents to reduce costs & improve performance!
Key Takeaways
Fine-tuning smaller open-source LLMs for App agents can reduce compute costs and API dependency, but requires action semantics learning beyond syntax
Full Article
Title: Beyond Syntax: Action Semantics Learning for App Agents
Abstract:
arXiv:2506.17697v3 Announce Type: replace Abstract: The recent development of Large Language Models (LLMs) enables the rise of App agents that interpret user intent and operate smartphone Apps through actions such as clicking and scrolling. While prompt-based solutions with proprietary LLM APIs show promising ability, they incur heavy compute costs and external API dependency. Fine-tuning smaller open-source LLMs solves these limitations. However, current supervised fine-tuning methods use a syn
Abstract:
arXiv:2506.17697v3 Announce Type: replace Abstract: The recent development of Large Language Models (LLMs) enables the rise of App agents that interpret user intent and operate smartphone Apps through actions such as clicking and scrolling. While prompt-based solutions with proprietary LLM APIs show promising ability, they incur heavy compute costs and external API dependency. Fine-tuning smaller open-source LLMs solves these limitations. However, current supervised fine-tuning methods use a syn
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