Mage-VL-Cowork

Mage-VL-Cowork bridges this gap. Trained end-to-end from an intermediate 4B Mage-VL checkpoint through a progressive five-stage specialize-then-unify curriculum, it combines screenshot-grounded GUI interaction (computer_use) and persistent code/terminal execution (bash) inside a single vision-language policy — with no external orchestrator. Upstream of training, we evolve the teacher harness (pruning redundant action routes and equipping domain libraries) and build Mage-CUA-Forge, a workflow-first synthesis engine that generates 293,806 tasks across 6,915 atomic skills while cutting recurring per-task generation cost by 82.6%.

At 4B parameters, Mage-VL-Cowork reaches 66.0% on OSWorld-Verified under a 50-step budget — achieving the best performance among 4B models and demonstrating that an efficient small VLM can excel at complex computer use, pushing the frontier of on-device intelligence. Across training stages, online reinforcement learning adds +10.16 percentage points over Hybrid SFT while simultaneously cutting mean model calls by 13.0% and output tokens by 25.4%.

✨ Highlights

• Best-in-class 4B hybrid computer use (66.0% OSWorld-Verified). Achieving the highest score at the 4B scale, Mage-VL-Cowork proves that an efficient small VLM can master end-to-end computer use — leading on Operating System tasks at 87.5%, reaching 62.7% on Multi-Application Workflows, and advancing the frontier of on-device intelligence.
• Internalized turn-level channel routing. Without an external multi-agent router, the policy dynamically selects computer_use for visual layouts, canvas editing, and live in-memory application state, and switches to bash for structured programmatic edits (spreadsheets, documents, presentations) and deterministic ground-truth verification.
• Evolved teacher harnesses amplify student distillation (+12.5 pp). Evolving and pruning the teacher scaffold before rollout (removing overlapping tools like xdotool and accessibility trees while pre-installing domain Python libraries) lifts the GPT-5.4 teacher to 92.0% with 29.2% fewer steps, which expands into a +12.5 pp student gain (37.6% vs. 25.1%) at the 4B scale.
• Workflow-first synthesis at −82.6% recurring cost. Mage-CUA-Forge authors reusable, skill-conditioned generation workflows and application-specific preparers once per skill rather than running open-ended agent loops per task — reducing token consumption by 93.3% (1,276K → 85K tokens/task) and expenditure from $0.906 to $0.158 per task.
• Specialize-then-unify five-stage curriculum (+20.44 pp). Starting from an intermediate 4B Mage-VL checkpoint, the model builds standalone visual grounding (30.6% → 56.9% on OSWorld-G; 78.8% → 87.2% on ScreenSpot-v2), GUI execution, and coding/terminal competence before unifying them via Hybrid SFT and asynchronous Online Hybrid RL (45.56% → 55.84% → 66.0%).
• Stronger and more compact after Online RL. Online GRPO with verifiable task rewards improves task accuracy by +10.16 pp over Hybrid SFT while compressing interaction length from 21.57 to 18.77 model calls (−13.0%) and output tokens from 7,374 to 5,499 per task (−25.4%).

📥 Model Overview

A single unified checkpoint, microsoft/Mage-VL-Cowork, handles visual grounding, multi-step GUI manipulation, programmatic file/system operations, and dynamic GUI-to-CLI channel switching.

🏗️ Architecture & Training Recipe

1. Evolved Hybrid CUA Harness

A strong teacher model does n