AI Research Introduces ‘PRInTS’: Reward Modeling for Long-Horizon Information Seeking
A new reward modeling technique, dubbed PRInTS (PRompting for long-horizon Information seeking with Task-based Scoring), has been introduced in a research paper (arXiv:2511.19314) submitted to arXiv on November 25, 2025. This advancement aims to enable AI agents to more effectively navigate and complete complex, long-horizon information-seeking tasks.
Unlike methods that focus on short-term predictions, PRInTS evaluates an AI’s performance based on the overall success of the task. This allows AI to learn more strategically, gathering information that contributes directly to achieving the ultimate goal. The research holds significant implications for the fields of Computation and Language (cs.CL) and Machine Learning (cs.LG), expanding AI’s capabilities in complex decision-making.
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