English(EN) AIS: Adaptive Importance Sampling for Quantized RL

新的采样方法稳定了大型语言模型的低精度强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-13 03:36

研究人员开发了自适应重要性采样（AIS）技术，以解决在大型语言模型强化学习中使用低精度 rollout 导致的训练不稳定性问题。该技术根据实时诊断动态调整梯度校正，平衡探索收益与偏差减少。当与 LLaDA-8B-Instruct 和 Qwen3 系列等模型集成时，AIS 在保持显著的低精度生成速度优势的同时，性能与更高精度的训练相当。 AI

影响通过低精度 rollout 稳定大型语言模型的训练，可能降低计算成本并提高效率。

排序理由该集群包含一篇详细介绍改进大型语言模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍改进大型语言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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arXiv stat.ML TIER_1 English(EN) · Ngai Wong · 2026-05-13 03:36

AIS: 量化强化学习的自适应重要性采样

Reinforcement learning (RL) for large language models (LLMs) is dominated by the cost of rollout generation, which has motivated the use of low-precision rollouts (e.g., FP8) paired with a BF16 trainer to improve throughput and reduce memory pressure. This introduces a rollout-tr…

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AIS: 量化强化学习的自适应重要性采样

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