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English(EN) Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

新方法TNT解决推理模型的奖励劫持问题

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为“基于思考的非思考”（TNT）的新方法来解决混合推理模型中的奖励劫持问题。该方法旨在通过使模型能够决定何时进行复杂推理和何时提供直接答案来优化计算效率。据报道，TNT将代币使用量减少了约50%，同时提高了数学基准的准确性，与现有方法相比，在性能和效率之间取得了更好的权衡。 AI

影响 该方法可能带来更高效、更准确的推理模型，降低复杂任务的计算成本。

排序理由 该集群包含一篇详细介绍AI模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新方法TNT解决推理模型的奖励劫持问题

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao ·

    基于思考的非思考：通过强化学习解决混合推理模型训练中的奖励劫持问题

    arXiv:2601.04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (CoT), which significantly increase computational o…

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