Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning
Researchers have developed a novel method called SPORT (Step-wise Preference Tuning) to train multimodal agents without relying on extensive human-annotated data. This approach uses an iterative process of task synthesis, step sampling, step verification, and preference tuning to enable agents to autonomously discover effective tool usage strategies. Evaluations on the GTA and GAIA benchmarks demonstrated significant improvements in agent performance, highlighting the method's generalization capabilities. AI
IMPACT Enables more efficient training of multimodal agents by reducing reliance on human annotation, potentially accelerating development and deployment.