LAMO uses role-oriented data synthesis and two-stage training (perplexity-weighted supervised fine-tuning plus reinforcement learning) to create scalable lightweight GUI agents that support both single-model and multi-agent orchestration.
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Towards Scalable Lightweight GUI Agents via Multi-role Orchestration
LAMO uses role-oriented data synthesis and two-stage training (perplexity-weighted supervised fine-tuning plus reinforcement learning) to create scalable lightweight GUI agents that support both single-model and multi-agent orchestration.