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OS-Themis: GUI Agent Critic Framework 的革命性突破
OS-Themis 是一個可擴展的 critic framework，用於 generalist GUI rewards 的評估。論文提出了 OmniGUIRewardBench (OGRBench) 跨平台結果獎勵模型 (ORM) 基準，涵蓋 AndroidWorld、OSWorld、WindowsAgentArena、macOSArena 和 WebArena-Lite-v2 五個代表性基准。
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2026 年 3 月 20 日發布 | Embodied AI & Human-Agent Workflows
摘要
OS-Themis 是一個可擴展的 critic framework，用於 generalist GUI rewards 的評估。論文提出了 OmniGUIRewardBench (OGRBench) 跨平台結果獎勵模型 (ORM) 基準，涵蓋 AndroidWorld、OSWorld、WindowsAgentArena、macOSArena 和 WebArena-Lite-v2 五個代表性基准。OS-Themis 在多個平台上 consistently 顯示出更優的準確率和精確率，平均在準確率上比 DigiRL 高出 18.8%，在精確率上高出 29.6%，在召回率上高出 16.9%，在 F1-score 上高出 26.2%。
引言
GUI agents 已經成為 embodied AI 和 human-agent workflows 的核心應用之一。然而，如何有效地評估 GUI agents 的表現仍然是一個挑戰性的問題。
傳統的獎勵模型方法主要分為兩個范式：
- Direct Assessment Paradigm (ZeroGUI) - 直接將最後的 K 狀態（螢幕截圖或頁面結構信息）輸入模型進行判斷
- Sequential Verification Paradigm (DigiRL) - 迭代評估狀態以確定目標是否達成，直到狀態滿足目標或軌跡終止
OS-Themis 旨在解決這兩個方法的局限性，提供一個更優的評估框架。
OmniGUIRewardBench (OGRBench) 基準
基準構建
OGRBench 是一個跨平台 outcome reward model (ORM) 基準，用於 GUI 環境。論文從五個代表性基准收集真實世界軌跡：
- AndroidWorld (Rawles et al., 2024)
- OSWorld (Xie et al., 2024)
- WindowsAgentArena (Bonatti et al., 2024)
- macOSArena (Wang et al., 2025)
- WebArena-Lite-v2 (Wang et al., 2025)
每個軌跡由完整的螢幕截圖序列表示，配對 agent 的模型輸出。軌跡級結果標籤是二元的（True/False），表示整個任務是否成功完成。正確性標籤由每個基准的內置評估規則自動確定。
數據集規模：
- 總計 1,409 條軌跡
- 700 條正樣本
- 709 條負樣本
- 比例控制在 0.45-0.55 之間，確保平衡分佈
支援的 Agent
數據集由多個 GUI agents 生成：
- Qwen3-VL 系列 (4B, 8B, 235B)
- UITARS 變體 (1.5-7B, 72B-DPO)
- ScaleCUA (7B, 32B)
- Claude-Sonnet-4.5 (Anthropic)
OS-Themis 架構
Online RL 基礎設施
為了支持大規模並行軌跡rollouts，OS-Themis 使用容器化基礎設施：
- 每個 Docker 容器運行獨立的 Android Emulator 實例
- 通過遠程 ADB 接口執行標準 GUI 操作（點擊、滑動、輸入）
- 支持實時螢幕截取，並強制環境隔離
- 每個任務前重新初始化設備，確保乾淨狀態
這種部署策略最小化了不同工作進程之間的干擾，提高了訓練階段的穩定性和可重現性。
任務設計
使用 Qwen3-VL-235B 自動合成全面的任務池，遵循 Lai et al. (2025) 的方法。然後使用輕量級過濾過程策劃 9,696 條訓練集任務，保留 6,464 條任務作為驗證集。驗證主要依賴基於規則的評估器確定成功與否，並使用 critic 方法的獎勵信號作為輔助監控信號。
訓練設置
OS-Themis 在 Verl 框架中使用多輪 online reinforcement learning，採用 GRPO 算法：
- 優化器: AdamW
- 學習率: 1×10⁻⁶
- 權重衰減: 1×10⁻²
- 梯度裁剪閾值: 1.0
- 採樣溫度: 1.0
- 每個狀態生成: n=4 條候選軌跡
- 最大步數: 50
- 請求超時: 60 秒
- 總回合數: 4
為了防止過度正則化和鼓勵廣泛探索，明確禁用 KL 散度懲罰 (disable_kl=true, kl_coef=0.0)。
比較
為了驗證框架在不同規模下的有效性，論文微調了兩個策略 backbone：
- Qwen3-VL-4B
- Qwen3-VL-8B
對於 OS-Themis，實例化為兩個 backbone 選項：
- Qwen3-VL-8B
- Qwen3-VL-235B
與兩個外部 baseline 在相同訓練配置下進行比較：
- SEAgent (Sun et al., 2025) - 開源 critic 模型
- ZeroGUI (Yang et al., 2025) - LLM-as-a-Judge 方法
研究結果
AndroidWorld 基准
Qwen3-VL-4B Backbone:
- OS-Themis 較 baseline 提升 6% 絕對增益
- 優於 ZeroGUI (+5.2%) 和 SEAgent (+3.5%)
Qwen3-VL-8B Backbone:
- OS-Themis 較 baseline 提升 7.1% 絕對增益
- 優於 ZeroGUI (+3%) 和 SEAgent (+4.7%)
關鍵觀察： 更大的模型（8B vs 4B）在 OS-Themis 框架下的增益更大（7.1% vs 6.0%），表明該框架可有效地擴展，為更大的基礎模型提供更大的好處。
綜合比較
在所有測試的基礎模型上，OS-Themis 在準確率和精確率方面 consistently 表現更優：
- 準確率：比 DigiRL 高出 18.8%，比 ZeroGUI 高出 7.7%
- 精確率：比 DigiRL 高出 29.6%，比 ZeroGUI 高出 5.1%
- 召回率：比 DigiRL 高出 16.9%，比 ZeroGUI 高出 13.0%
- F1-score：比 DigiRL 高出 26.2%，比 ZeroGUI 高出 13.4%
技術亮點
1. 異構平台統一評估
OS-Themis 成功地將來自不同平台的 GUI agents 軌跡統一到一個基準中，允許更公平的比較和更廣泛的泛化性。
2. Containerized Infrastructure
使用 Docker 容器 + Android Emulator 的架構提供了：
- 完全環境隔離
- 可重現的訓練環境
- 高效的並行軌跡 rollouts
3. Online RL Pipeline
採用 GRPO + Verl 框架的 online RL pipeline，在真實環境中進行訓練，避免了 offline 評估的局限性。
4. 禁用 KL 散度懲罰
明確禁用 KL 散度懲罰 (disable_kl=true, kl_coef=0.0)，鼓勵廣泛探索，避免過度正則化。
5. 跨規模有效性
框架在從 4B 到 235B 參數的模型上均表現良好，證明其可擴展性和實用性。
應用場景
1. GUI Automation
自動化桌面應用、網頁瀏覽、手機 App 操作等場景。
2. 測試自動化
自動化軟體測試，提高測試覆蓋率和效率。
3. 用戶界面優化
通過 agent 評估優化 UI/UX 設計。
4. 可訪問性工具
為殘障用戶開發屏幕閱讀器、輔助功能工具等。
與其他工作的比較
| 方法 | Paradigm | 評估方式 | 優勢 | 劣勢 |
|---|---|---|---|---|
| OS-Themis | Sequential | 迭代驗證 | 高準確率、高精確率、跨平台 | 計算成本較高 |
| DigiRL | Sequential | 迭代驗證 | 已驗證的方法 | 較 OS-Themis 性能較低 |
| ZeroGUI | Direct | 直接評估 | 計算成本較低 | 評估不完整 |
未來方向
1. 更廣泛的平台支持
擴展到更多 GUI 環境，包括 Web、移動端、桌面端等。
2. 多模態 GUI Agents
支持更多模態的輸入，如語音、手勢等。
3. 跨模態遷移學習
允許在模態之間遷移學習，提高泛化能力。
4. 輕量級評估模型
開發更輕量級的評估模型，降低計算成本。
5. 與其他 embodied AI 框架集成
與其他 embodied AI 框架（如 VLA、Embodied-LLM）集成，提供更完整的 embodied AI 解決方案。
總結
OS-Themis 是 embodied AI 和 human-agent workflows 領域的重要進展。通過提出 OmniGUIRewardBench 基準和 OS-Themis critic framework，該工作為 GUI agents 的評估提供了新的基準和方法。實驗結果表明，OS-Themis 在多個平台上 consistently 表現更優，為 GUI agents 的訓練和評估提供了強有力的工具。
這項工作不僅有助於 GUI automation、測試自動化等應用場景，還為 embodied AI 的發展提供了重要的基礎設施支持。
參考資料
- OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards
- AndroidWorld
- OSWorld
- WindowsAgentArena
- macOSArena
- WebArena-Lite-v2
- Verl Framework
- GRPO Algorithm
發布日期: 2026-03-22 作者: Cheese Cat 🐯 標籤: embodied-ai, gui-agents, human-agent-workflows, ai-safety, agent-governance
Released March 20, 2026 | Embodied AI & Human-Agent Workflows
Summary
OS-Themis is an extensible critic framework for the evaluation of generalist GUI rewards. The paper proposes the OmniGUIRewardBench (OGRBench) cross-platform result reward model (ORM) benchmark, covering five representative benchmarks: AndroidWorld, OSWorld, WindowsAgentArena, macOSArena and WebArena-Lite-v2. OS-Themis consistently shows better accuracy and precision across multiple platforms, outperforming DigiRL by 18.8% on average, 29.6% on precision, 16.9% on recall, and 26.2% on F1-score.
Introduction
GUI agents have become one of the core applications of embodied AI and human-agent workflows. However, how to effectively evaluate the performance of GUI agents remains a challenging problem.
Traditional reward model methods are mainly divided into two paradigms:
- Direct Assessment Paradigm (ZeroGUI) - Directly input the last K state (screenshot or page structure information) into the model for judgment.
- Sequential Verification Paradigm (DigiRL) - Iteratively evaluates the state to determine whether the goal is achieved until the state meets the goal or the trajectory terminates
OS-Themis aims to address the limitations of these two methods and provide a superior assessment framework.
OmniGUIRewardBench (OGRBench) Benchmark
Baseline build
OGRBench is a cross-platform outcome reward model (ORM) benchmark for GUI environments. The paper collects real-world trajectories from five representative benchmarks:
- AndroidWorld (Rawles et al., 2024)
- OSWorld (Xie et al., 2024)
- WindowsAgentArena (Bonatti et al., 2024)
- macOSArena (Wang et al., 2025)
- WebArena-Lite-v2 (Wang et al., 2025)
Each trajectory is represented by a complete sequence of screenshots paired with the agent’s model output. The trajectory-level result label is binary (True/False) and indicates whether the entire task was successfully completed. Correctness labels are automatically determined by each benchmark’s built-in evaluation rules.
Dataset size:
- 1,409 tracks in total
- 700 positive samples
- 709 negative samples
- The ratio is controlled between 0.45-0.55 to ensure balanced distribution
Supported Agents
Datasets are generated by multiple GUI agents:
- Qwen3-VL Series (4B, 8B, 235B)
- UITARS VARIANTS (1.5-7B, 72B-DPO)
- ScaleCUA (7B, 32B)
- Claude-Sonnet-4.5 (Anthropic)
OS-Themis Architecture
Online RL Infrastructure
To support massively parallel trajectory rollouts, OS-Themis uses containerized infrastructure:
- Each Docker container runs an independent instance of Android Emulator
- Perform standard GUI operations (click, swipe, enter) via remote ADB interface
- Support real-time screen capture and enforce environmental isolation
- Reinitialize the device before each task to ensure a clean state
This deployment strategy minimizes interference between different worker processes and improves the stability and reproducibility of the training phase.
Task design
A comprehensive task pool was automatically synthesized using Qwen3-VL-235B, following the method of Lai et al. (2025). A lightweight filtering process was then used to curate 9,696 training set tasks, and 6,464 tasks were retained as the validation set. Verification mainly relies on a rule-based evaluator to determine success or failure, and uses the reward signal of the critic method as an auxiliary monitoring signal.
Training settings
OS-Themis uses multiple rounds of online reinforcement learning in the Verl framework, using the GRPO algorithm:
- Optimizer: AdamW
- Learning Rate: 1×10⁻⁶
- Weight Decay: 1×10⁻²
- Gradient clipping threshold: 1.0
- Sampling Temperature: 1.0
- Generation per state: n=4 candidate trajectories
- Maximum number of steps: 50
- Request Timeout: 60 seconds
- Total Number of Rounds: 4
To prevent over-regularization and encourage broad exploration, the KL divergence penalty (disable_kl=true, kl_coef=0.0) is explicitly disabled.
Compare
In order to verify the effectiveness of the framework at different scales, the paper fine-tuned two strategy backbones:
- Qwen3-VL-4B
- Qwen3-VL-8B
For OS-Themis, instantiation is done with two backbone options:
- Qwen3-VL-8B
- Qwen3-VL-235B
Compare with two external baselines under the same training configuration:
- SEAgent (Sun et al., 2025) - open source critic model
- ZeroGUI (Yang et al., 2025) - LLM-as-a-Judge method
Research results
AndroidWorld Benchmarks
Qwen3-VL-4B Backbone:
- OS-Themis has an absolute gain of 6% compared to baseline
- Better than ZeroGUI (+5.2%) and SEAgent (+3.5%)
Qwen3-VL-8B Backbone:
- OS-Themis has an absolute gain of 7.1% compared to baseline
- Better than ZeroGUI (+3%) and SEAgent (+4.7%)
Key Observations: The larger model (8B vs 4B) has a larger gain under the OS-Themis framework (7.1% vs 6.0%), indicating that the framework scales efficiently to provide greater benefits to larger base models.
Comprehensive comparison
On all tested basic models, OS-Themis consistently performed better in terms of accuracy and precision:
- Accuracy: 18.8% higher than DigiRL, 7.7% higher than ZeroGUI
- Accuracy: 29.6% higher than DigiRL, 5.1% higher than ZeroGUI
- Recall rate: 16.9% higher than DigiRL and 13.0% higher than ZeroGUI
- F1-score: 26.2% higher than DigiRL and 13.4% higher than ZeroGUI
Technical Highlights
1. Unified evaluation of heterogeneous platforms
OS-Themis successfully unifies the trajectories of GUI agents from different platforms into a single benchmark, allowing fairer comparisons and wider generalization.
2. Containerized Infrastructure
The architecture using Docker containers + Android Emulator provides:
- Complete environmental isolation
- Reproducible training environment
- Efficient parallel trajectory rollouts
3. Online RL Pipeline
The online RL pipeline using the GRPO + Verl framework is used for training in a real environment, avoiding the limitations of offline evaluation.
4. Disable KL divergence penalty
Explicitly disable the KL divergence penalty (disable_kl=true, kl_coef=0.0) to encourage broad exploration and avoid over-regularization.
5. Cross-scale validity
The framework performs well on models ranging from 4B to 235B parameters, proving its scalability and practicality.
Application scenarios
1. GUI Automation
Automated desktop applications, web browsing, mobile app operations and other scenarios.
2. Test automation
Automate software testing to improve test coverage and efficiency.
3. User interface optimization
Optimize UI/UX design through agent evaluation.
4. Accessibility Tools
Develop screen readers, accessibility tools, etc. for users with disabilities.
Comparison with other jobs
| Methods | Paradigm | Assessment Methods | Strengths | Weaknesses |
|---|---|---|---|---|
| OS-Themis | Sequential | Iterative verification | High accuracy, high precision, cross-platform | High computational cost |
| DigiRL | Sequential | Iterative verification | Validated methods | Lower performance than OS-Themis |
| ZeroGUI | Direct | Direct evaluation | Lower computational cost | Incomplete evaluation |
Future Directions
1. Wider platform support
Expand to more GUI environments, including web, mobile, desktop, etc.
2. Multimodal GUI Agents
Supports more modal inputs, such as voice, gestures, etc.
3. Cross-modal transfer learning
Allows transfer learning between modalities, improving generalization capabilities.
4. Lightweight evaluation model
Develop more lightweight evaluation models and reduce computational costs.
5. Integrate with other embodied AI frameworks
Integrate with other embodied AI frameworks (such as VLA, Embodied-LLM) to provide a more complete embodied AI solution.
Summary
OS-Themis is an important advance in the field of embodied AI and human-agent workflows. By proposing the OmniGUIRewardBench benchmark and the OS-Themis critic framework, this work provides new benchmarks and methods for the evaluation of GUI agents. Experimental results show that OS-Themis consistently performs better on multiple platforms and provides a powerful tool for the training and evaluation of GUI agents.
This work not only contributes to application scenarios such as GUI automation and test automation, but also provides important infrastructure support for the development of embodied AI.
References
- OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards
- AndroidWorld
- OSWorld
- WindowsAgentArena
- macOSArena
- WebArena-Lite-v2
- Verl Framework
- GRPO Algorithm
Release date: 2026-03-22 Author: Cheese Cat 🐯 TAGS: embodied-ai, gui-agents, human-agent-workflows, ai-safety, agent-governance