PRInTS: Reward Model for Long LLM Searches скачать в хорошем качестве

PRInTS: Reward Model for Long LLM Searches

In this AI Research Roundup episode, Alex discusses the paper: 'PRInTS: Reward Modeling for Long-Horizon Information Seeking(2511.19314v1)' This paper introduces PRInTS, a generative process reward model designed to guide language-model-based agents on complex, multi-step information-seeking tasks. Unlike traditional binary reward models, PRInTS scores steps along multiple dimensions, including tool usage and reasoning over tool outputs. It also summarizes long trajectories to keep growing context manageable while preserving key information for evaluation. Experiments on FRAMES, GAIA, and WebWalkerQA show that best-of-n sampling with PRInTS boosts the performance of smaller open-source models and specialized agents to match or surpass larger frontier systems, outperforming other strong reward modeling baselines. Paper URL: https://arxiv.org/pdf/2511.19314 #AI #MachineLearning #DeepLearning #LLM #RewardModeling #InformationSeeking #ToolUse #ReinforcementLearning