新的GIFT方法通过过滤蒸馏梯度来改进LLM推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员推出了一种新颖的训练大型语言模型（LLM）以完成复杂推理任务的方法，名为GIFT。GIFT通过分析结果导向强化学习（RL）和token级蒸馏之间梯度的几何关系，解决了它们之间的冲突。这种分析将蒸馏梯度分解为与RL梯度平行和垂直的分量，从而能够对token级指导进行原则性排序。在数学和逻辑推理基准上的实验表明，GIFT的性能始终优于现有方法，并取得了显著的提升。 AI

影响通过优化训练信号，提高了LLM在复杂推理任务上的性能。

排序理由该集群包含一篇研究论文，详细介绍了一种训练大型语言模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Shijie Li, Yilin Gao, Tieyuan Chen, Yun Luo, Weiyao Lin, Hang Yu · 2026-10-23 04:00

GIFT: Gradient Informed Filtering for Distillation Augmented Reasoning RL

arXiv:2610.30796v1 Announce Type: new Abstract: Training large language models for complex reasoning tasks relies on two complementary signals: sparse, outcome-grounded reinforcement learning (RL) for correctness, and dense, token-level distillation for guidance. However, complem…

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GIFT: Gradient Informed Filtering for Distillation Augmented Reasoning RL

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