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Generalizing Verifiable Instruction Following

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The introduction of IFBench marks a significant advancement in assessing how well language models and chatbots can follow intricate human instructions. While existing models have shown proficiency in a narrow range of verifiable constraints, they struggle to generalize to new, unseen instructions. This limitation hampers effective human-AI interaction, making the development of robust benchmarks essential. The study not only presents IFBench, which includes 58 diverse constraints, but also emphasizes the potential of reinforcement learning with verifiable rewards (RLVR) to enhance these models' performance. By releasing 29 additional hand-annotated training constraints and verification functions, the research aims to provide a comprehensive framework for improving precise instruction following, ultimately fostering more effective communication between humans and AI.
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