深度寻求：通用奖励建模的推理时间扩展原文：https://arxiv.org/abs/2504.02495阅读时间：5 分钟分数：152

深度寻求：通用奖励建模的推理时间扩展
原文：https://arxiv.org/abs/2504.02495
阅读时间：5 分钟
分数：152

原标题：DeepSeek: Inference-Time Scaling for Generalist Reward Modeling 该论文标题为《推理时间扩展的通用奖励建模》，由Zijun Liu等人于2025年4月3日提交。研究的主要内容是探讨在大规模语言模型（LLMs）后训练中应用强化学习（RL）的有效性，尤其是如何通过合适的学习方法实现有效的推理时间扩展。论文指出，当前RL的一大挑战是如何在各种领域中为LLMs获取准确的奖励信号。而该研究旨在通过增加推理计算来改善通用查询的奖励建模，即提升通…

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深度寻求：通用奖励建模的推理时间扩展
原文：https://arxiv.org/abs/2504.02495
阅读时间：5 分钟
分数：152

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