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English(EN) Consolidating Reasoning with Test-Time Training

新方法旨在通过优化思考过程来提高LLM推理能力 · 已追踪3个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了新的方法，通过优化大型语言模型（LLM）的思考过程来提高其推理能力。一种方法TTT-Consolidate使用多智能体推理轨迹作为训练数据，以增强模型后续的推理能力，在数学和STEM基准测试中取得了显著的进步。另一种策略Concise-9B通过自训练专注于去除模型思考过程中“松弛”或不必要的token，以更少的token实现了相当的准确性。第三项研究探索了在较小模型中进行开放式推理的测试时缩放，发现虽然它提高了数学问题的性能，但对自由文本生成的好处有限。 AI

影响 这些进展可能带来更高效、更准确的LLM，尤其是在数学和STEM问题解决等复杂推理任务中。

排序理由 arXiv上发表了多篇研究论文，详细介绍了提高LLM推理能力的新方法。

在 arXiv cs.CL 阅读 →

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新方法旨在通过优化思考过程来提高LLM推理能力 · 已追踪3个来源

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arXiv上发表了多篇研究论文，详细介绍了提高LLM推理能力的新方法。
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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Haotian Wu, Zeming Chen, Hao Zhao, Antoine Bosselut ·

    Consolidating Reasoning with Test-Time Training

    arXiv:2611.02104v1 Announce Type: new Abstract: Effective multi-agent collaboration requires finalizing solutions from reasoning that is distributed across the individual attempts of many agents. However, useful intermediate reasoning can remain unexploited when collaboration is …

  2. arXiv cs.CL TIER_1 English(EN) · Adam Y. Ahmed, Abdullah G. Elafifi ·

    小模型规模下的开放式推理的测试时缩放

    arXiv:2611.02208v1 Announce Type: cross Abstract: A vote over a model's samples can only help when the samples disagree. For open-ended generation the binding question is therefore not how to combine samples but whether a model produces samples worth combining, a question test-ti…

  3. arXiv cs.CL TIER_1 English(EN) · Alexander Boesgaard Lorup ·

    Concise-9B：消除推理模型思考中的冗余

    arXiv:2611.02278v1 Announce Type: cross Abstract: Reasoning models answer better when they think longer, but a thinking trace keeps going well after its answer has formed. We measure this slack in Qwen3.5-9B and remove most of it with concise self-training: two rounds of on-polic…

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