New ARG method boosts reasoning model training on Qwen3 benchmarks

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种名为自适应参考引导（ARG）的新方法，以改进强化学习中推理模型的训练。ARG通过前缀续写策略，在提供正确参考轨迹和允许模型自行生成之间取得平衡。该方法旨在固定生成预算内构建正确的轨迹。在五个数学推理基准上对Qwen3-4B和Qwen3-8B模型进行的实验表明，ARG在评估方法中实现了最高的聚合pass@12率。 AI

影响这项研究可能有助于更有效、更高效地训练用于复杂推理任务的AI模型。

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]

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New ARG method boosts reasoning model training on Qwen3 benchmarks

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arXiv cs.AI TIER_1 English(EN) · Hanyu Wang, Nakul Agarwal, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Makoto Fukushima, Jinghui Chen, Vaishnav Tadiparthi · 2026-10-09 04:00

在推理强化学习的轨迹回放中平衡参考引导与自由生成

arXiv:2610.11128v1 Announce Type: new Abstract: A verified reference solution provides a correct trajectory for training a reasoning model. Alternatively, a prefix of the reference can guide the model in generating a trajectory of its own. How much reference guidance should we pr…

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在推理强化学习的轨迹回放中平衡参考引导与自由生成

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