# ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents

arXiv: https://arxiv.org/abs/2508.14040 · HTML: https://mlsanity.com/paper/2508.14040/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning.
- Builds on 47 of its 67 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Placed in

- LLM reasoning / [Reinforcement learning for reasoning](https://mlsanity.com/lr/reinforcement-learning-for-reasoning/)
- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 30 of 47)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025)
- [Group-in-Group Policy Optimization for LLM Agent Training](https://mlsanity.com/paper/2505.10978/) (2025)
- [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://mlsanity.com/paper/2410.23218/) (2024)
- [Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction](https://mlsanity.com/paper/2412.04454/) (2024)
- [AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning](https://mlsanity.com/paper/2505.24298/) (2025)
- [Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale](https://mlsanity.com/paper/2409.08264/) (2024)

## Built on by (most cited first, 30 of 50)

- [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://mlsanity.com/paper/2509.02547/) (2025)
- [A Survey of Reinforcement Learning for Large Reasoning Models](https://mlsanity.com/paper/2509.08827/) (2025)
- [ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data](https://mlsanity.com/paper/2509.15221/) (2025)
- [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026)
- [MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents](https://mlsanity.com/paper/2509.18119/) (2025)
- [Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning](https://mlsanity.com/paper/2605.26684/) (2026)
- [OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents](https://mlsanity.com/paper/2510.24563/) (2025)
- [Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation](https://mlsanity.com/paper/2509.23866/) (2025)
- [OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent](https://mlsanity.com/paper/2601.07779/) (2026)
- [LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training](https://mlsanity.com/paper/2510.14969/) (2025)
- [Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems](https://mlsanity.com/paper/2602.08847/) (2026)
- [VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents](https://mlsanity.com/paper/2509.07553/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents](https://mlsanity.com/paper/2604.11784/) (2026)
- [R-WoM: Retrieval-augmented World Model For Computer-use Agents](https://mlsanity.com/paper/2510.11892/) (2025)
- [Adaptive Milestone Reward for GUI Agents](https://mlsanity.com/paper/2602.11524/) (2026)
- [From Question Answering to Task Completion: A Survey on Agent System and Harness Design](https://mlsanity.com/paper/2606.20683/) (2026)
- [ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration](https://mlsanity.com/paper/2509.21823/) (2025)
- [Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding](https://mlsanity.com/paper/2605.00642/) (2026)
- [OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards](https://mlsanity.com/paper/2603.19191/) (2026)
- [Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration](https://mlsanity.com/paper/2509.11067/) (2025)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents](https://mlsanity.com/paper/2607.04425/) (2026)
- [SCUBA: Salesforce Computer Use Benchmark](https://mlsanity.com/paper/2509.26506/) (2025)
- [Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It](https://mlsanity.com/paper/2606.26027/) (2026)
- [SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL](https://mlsanity.com/paper/2607.11185/) (2026)
- [OS-Oracle: A Comprehensive Framework for Cross-Platform GUI Critic Models](https://mlsanity.com/paper/2512.16295/) (2025)
- [Autonomous Continual Learning for Environment Adaptation of Computer-Use Agents](https://mlsanity.com/paper/2602.10356/) (2026)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning](https://mlsanity.com/paper/2510.20548/) (2025)

## Abstract (arXiv)

We introduce ComputerRL, a framework for autonomous desktop intelligence that enables agents to operate complex digital workspaces skillfully. ComputerRL features the API-GUI paradigm, which unifies programmatic API calls and direct GUI interaction to address the inherent mismatch between machine agents and human-centric desktop environments. Scaling end-to-end RL training is crucial for improvement and generalization across diverse desktop tasks; however, it remains challenging due to environmental inefficiency and instability during extended training. To support scalable and robust training, we develop a distributed RL infrastructure capable of orchestrating thousands of parallel virtual desktop environments to accelerate large-scale online RL. Furthermore, we propose Entropulse, a training strategy that alternates reinforcement learning with supervised fine-tuning, effectively mitigating entropy collapse during extended training runs. We employ ComputerRL on open models GLM-4-9B-0414 and GLM-4.1V-9B-Thinking, and evaluate them on the OSWorld benchmark. The AutoGLM-OS-9B achieves a new state-of-the-art accuracy of 48.9%, demonstrating significant improvements for general agents in desktop automation. Our code and the new OfficeWorld benchmark are available at https://github.com/thudm/ComputerRL. The algorithm and framework are adopted in building AutoGLM (Liu et al., 2024b).