Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark

Understand

The development of Generalist Virtual Agents (GVAs) has shown significant promise in autonomous task execution.

• However, current training paradigms face critical limitations, including reliance on outcome supervision and labor-intensive human annotations.
• To address these challenges, we propose Similar, a Step-Wise Multi-Dimensional Generalist Reward Model, which offers fine-grained signals for agent training and can choose better action for inference-time scaling.
• Specifically, we begin by systematically defining five dimensions for evaluating agent actions.