English(EN) HARTS: Efficient Agentic Reinforcement Learning for Hybrid-Attention Models over Arbitrary Rollout Trees

HARTS系统加速了混合注意力模型的智能强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了HARTS，一个旨在提高混合注意力模型智能强化学习（RL）效率的新系统。HARTS通过采用前缀压缩和数据并行副本优化调度等技术，解决了在不规则展开树中重新计算共享前缀的挑战。该系统实现了激活重计算和状态恢复，在源自SWE-bench任务的智能RL工作负载上实现了高达4.8倍的显著加速。 AI

影响该系统可能导致更高效的复杂AI代理训练，从而加速能够执行复杂任务的AI的开发。

排序理由该集群描述了一篇关于AI模型训练新系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Boyuan Meng (Ant Group, China), Peihua Bao (Ant Group, China), Hong Liu (Ant Group, China), Xiaowei Zhu (Ant Group, China), Chao Wang (Ant Group, China), Gen Li (Ant Group, China), Zhenxuan Pan (Ant Group, China) · 2026-08-31 04:00

HARTS：混合注意力模型在任意展开树上的高效智能强化学习

arXiv:2608.28158v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) often produces irregular rollout trees with shared histories. Training root-to-leaf trajectories independently recomputes these shared prefixes. Existing systems primarily target full-attention mo…

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报道来源 [1]

HARTS：混合注意力模型在任意展开树上的高效智能强化学习

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