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[2602.14093] GUI-GENESIS: Automated Synthesis of Efficient Environments with Verifiable Rewards for GUI Agent Post-Training
Summary
The paper presents GUI-GENESIS, a framework for automating the synthesis of efficient training environments for GUI agents, enhancing performance and reducing costs significantly.
Why It Matters
GUI-GENESIS addresses critical challenges in training GUI agents by providing verifiable rewards and reducing latency and costs. This innovation could lead to more effective AI systems capable of better generalization and planning in real-world applications, ultimately advancing the field of artificial intelligence.
Key Takeaways
- GUI-GENESIS synthesizes lightweight web environments for GUI training.
- It reduces training costs by over $28,000 per epoch.
- Agents trained with GUI-GENESIS outperform traditional models on real-world tasks.
- The framework enables agents to synthesize environments they cannot yet solve.
- Deterministic reward signals eliminate visual estimation noise.
Computer Science > Artificial Intelligence arXiv:2602.14093 (cs) [Submitted on 15 Feb 2026] Title:GUI-GENESIS: Automated Synthesis of Efficient Environments with Verifiable Rewards for GUI Agent Post-Training Authors:Yuan Cao, Dezhi Ran, Mengzhou Wu, Yuzhe Guo, Xin Chen, Ang Li, Gang Cao, Gong Zhi, Hao Yu, Linyi Li, Wei Yang, Tao Xie View a PDF of the paper titled GUI-GENESIS: Automated Synthesis of Efficient Environments with Verifiable Rewards for GUI Agent Post-Training, by Yuan Cao and 11 other authors View PDF HTML (experimental) Abstract:Post-training GUI agents in interactive environments is critical for developing generalization and long-horizon planning capabilities. However, training on real-world applications is hindered by high latency, poor reproducibility, and unverifiable rewards relying on noisy visual proxies. To address the limitations, we present GUI-GENESIS, the first framework to automatically synthesize efficient GUI training environments with verifiable rewards. GUI-GENESIS reconstructs real-world applications into lightweight web environments using multimodal code models and equips them with code-native rewards, executable assertions that provide deterministic reward signals and eliminate visual estimation noise. Extensive experiments show that GUI-GENESIS reduces environment latency by 10 times and costs by over $28,000 per epoch compared to training on real applications. Notably, agents trained with GUI-GENESIS outperform the base model by 14.54% ...
