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The research team then used that data to fine-tune Qwen2.5-VL 32B via supervised fine-tuning, followed by reinforcement learning using a PPO-based semi-online asynchronous pipeline (200 steps, batch size 64, learning rate 1e-6). The resulting model achieved a 56.3% success rate on the OSWorld-Verified benchmark — competitive with existing methods for a 32B parameter base model with no task-specific tuning.
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