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One Model to Critique Them All: Rewarding Agentic Tool-Use via Efficient Reasoning

arXiv — cs.CL•Friday, October 31, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A new development in AI research introduces ToolRM, a family of reward models designed to enhance the alignment of large language models with human preferences, particularly in tool-use scenarios. This innovation addresses a significant gap in the field, as existing models have struggled with function-calling tasks. By implementing a novel pipeline for constructing pairwise preference data, ToolRM aims to facilitate more capable agentic AI, which could lead to advancements in how AI systems interact with tools and perform complex tasks. This progress is crucial for the future of AI, as it promises to improve the efficiency and effectiveness of AI applications.
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