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English(EN) How Should a Prompt Optimizer Spend a Tight Budget? BudgetAPO with Noise-Adaptive Evaluation

新研究探讨机器学习任务的预算优化 · 跟踪到2个来源

两篇新研究论文探讨了机器学习任务的预算优化策略。第一篇论文“BudgetAPO”介绍了一种新颖的单阶段优化器,专为预算紧张和速率受限的API场景设计,在各种基准测试中表现优于GEPA等现有方法。第二篇论文“Optimally Pacing Budget Spending and Learning”为对抗性设置下的预算约束在线学习建立了近乎最优的遗憾界限,并扩展到在线资源分配问题。 AI

影响 这些进展可能导致在训练和部署大型语言模型及其他机器学习系统时,计算资源的使用更加高效和经济。

排序理由 两篇学术论文,提出了机器学习背景下预算优化的新算法和理论界限。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新研究探讨机器学习任务的预算优化 · 跟踪到2个来源

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两篇学术论文,提出了机器学习背景下预算优化的新算法和理论界限。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Haoyue Liu, Zhichao Wang, Huanyu Yan, Xiaoying Tang ·

    预算紧张时,提示优化器应如何花费?具有噪声自适应评估的BudgetAPO

    arXiv:2610.05671v2 Announce Type: replace Abstract: Automatic prompt optimization (APO) has been widely employed to adapt large language models without updating their weights, yielding promising results. However, existing methods such as GEPA and OPRO assume hundreds to thousands…

  2. arXiv cs.LG TIER_1 English(EN) · Mark Braverman, Jingyi Liu, Jieming Mao, Jon Schneider, Eric Xue ·

    优化预算支出与学习的节奏

    arXiv:2610.11074v1 Announce Type: new Abstract: We establish near-optimal regret bounds for budget-constrained online learning against arbitrary classes of budget-pacing experts in the adversarial setting. In particular, given any class of $F$ experts and a candidate budget pacin…

  3. Medium — MLOps tag TIER_1 English(EN) · Shrinath Suresh ·

    A-Z 使用 MLflow:设置预算策略

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/@shrinath.suresh/a-z-with-mlflow-budget-policy-587fc06b8015?source=rss------mlops-5"><img src="https://cdn-images-1.medium.com/max/1917/1*B7bk7i4fcTqE5beFOgilgg.png" width="1917" /></a></p><p c…