English(EN) Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression

新的TRAAC方法通过压缩思维过程优化LLM推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种新的训练后强化学习方法，称为TRAAC（Think Right with Adaptive, Attentive Compression），以解决大型语言模型中的思考不足和过度思考问题。TRAAC利用自注意力机制来识别和修剪冗余的推理步骤，同时学习根据任务难度分配推理预算。将其应用于Qwen3-4B模型后，TRAAC在AIME、AMC、GPQA-D和BBEH等各种任务上实现了显著的准确性提升，同时缩短了推理步骤的长度。 AI

影响该方法有望实现更高效、更准确的LLM推理，降低计算成本并提高复杂任务的性能。

排序理由该集群包含一篇学术论文，详细介绍了一种改进LLM推理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Joykirat Singh, Justin Chih-Yao Chen, Archiki Prasad, Elias Stengel-Eskin, Akshay Nambi, Mohit Bansal · 2026-10-01 04:00

Think Right：通过自适应、注意力压缩学习缓解过度思考和思考不足

arXiv:2510.01581v2 Announce Type: replace-cross Abstract: Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in line with task difficulty. On one hand, short reasoning (underthinking) leads to e…

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Think Right：通过自适应、注意力压缩学习缓解过度思考和思考不足

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