ComputerRL

API‑GUI action space. Moves beyond human‑centric GUIs by combining programmatic APIs with direct GUI control. LLMs help auto‑generate app‑specific APIs via requirement analysis, implementation, and unit tests, lowering the cost of adding new tools.

Massively parallel desktop env. A refactored Ubuntu VM cluster (qemu‑in‑docker + gRPC) delivers thousands of concurrent instances with improved stability, monitoring, and AgentBench‑compatible interfaces, enabling large‑scale online RL.

Fully asynchronous RL. Built on AgentRL with decoupled actors/trainers, dynamic batching, and bounded replay to reduce off‑policy bias and maximize GPU utilization during long‑horizon desktop rollouts.

Entropulse training. After an initial step‑level GRPO phase with verifiable, rule‑based rewards, successful rollouts are distilled via SFT to restore entropy, then RL resumes, yielding higher rewards and sustained improvements.

Results and analysis. AUTOGLM‑OS‑9B reaches 48.1% on OSWorld and 47.3% on OSWorld‑Verified, outperforming OpenAI CUA o3, UI‑TARS‑1.5, and Claude Sonnet 4, while using up to one‑third the action steps of strong baselines; ablations show API‑GUI and multi‑stage training drive the gains. Error sources cluster into multi‑app coordination, vision, and operational slips.