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Daytona（产业 SoCC 2026）发布：编码智能体强化学习基础设施优化研究
Rollout 基建税差 110×：Daytona（产业 SoCC 2026），1M×150-step worker-hour 差 1.8×
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AI 导读
编码 agent RL 把执行基建当背景实现细节，错失可观效率。
一手信源
学术论文·arXiv:2607.01415
Daytona（产业 SoCC 2026）发布 (arXiv:2607.01415)
“View PDF HTML (experimental) Abstract: Coding-agent reinforcement learning treats execution infrastructure as a background implementation de”
正文
Abstract:Coding-agent reinforcement learning treats execution infrastructure as a background implementation detail, despite relying on large numbers of interactive software rollouts. This is a missed opportunity: measuring infrastructure overhead can reveal practical efficiency gains for RL post-training, where small per-rollout savings compound at scale. We present a comparative study of four execution substrates: single containers, hosted sandboxes, Kubernetes-orchestrated containers, and cloud virtual machines. We find up to $110\times$ variation in cold-start latency and a $1.8\times$ spread in projected worker-hours for one million 150-step trajectories. Our results suggest that future coding-agent RL systems should optimize execution substrates as part of the training system itself, not merely as deployment plumbing.
| Comments: | Preprint. 6 pages, 6 figures, 2 tables. Submitted to ACM SoCC 2026 |
| Subjects: | Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC) |
| ACM classes: | C.4; D.4.8; I.2.6 |
| Cite as: | arXiv:2607.01415 [cs.LG] |
| (or arXiv:2607.01415v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01415 arXiv-issued DOI via DataCite |
Submission history
From: Daniel Graviet [view email]
[v1]
Wed, 1 Jul 2026 19:20:46 UTC (214 KB)
来源：Agent Control Flash News · arxiv.org
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