English(EN) DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards

DUET方法优化RLVR令牌预算，提高质量和速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

研究人员开发了DUET，一种用于优化可验证奖励（RLVR）的强化学习中令牌预算分配的新颖方法。DUET联合调整提示选择和回滚长度，优于之前仅控制一个维度的现有方法。该方法显著提高了推理质量并减少了训练时间，即使仅使用一半的令牌预算。 AI

影响 DUET的令牌预算优化方法可以显著加速LLM训练，并提高各种基准测试中模型的质量。

排序理由该集群包含一篇详细介绍优化AI训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Haoyu Hu, Xuandong Zhao, Xuhai "Orson'' Xu, Nori Jacoby · 2026-10-31 04:00

DUET：为具有可验证奖励的强化学习优化令牌预算分配

arXiv:2605.08441v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating the computational cost. The overall token budget can be controlled along…