English(EN) Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?

小型语言模型未能达到微任务资格基准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

一篇新的arXiv论文研究了小型语言模型（SLM）在代理组合中执行微任务的资格。研究发现，即使在最优配置和FP16精度下，测试的Qwen模型也未能达到定义的资格阈值。量化到4位精度会进一步降低性能，差距与模型大小相关而非精度。研究表明，SLM应置于满足所需阈值的基线系统之后使用，而不是作为这些微任务的主要组成部分。 AI

影响强调了当前SLM在代理组合中的局限性，暗示需要更好的基线或模型改进。

排序理由学术论文发表在arXiv上，详细介绍了模型在特定基准上的性能。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Jundong Hu, Shekar Ramachandran · 2026-10-02 04:00

衡量微任务资格差距：现成的SLM何时足以用于Agent Harness？

arXiv:2610.00025v1 Announce Type: new Abstract: Agent harnesses increasingly want to run small language models (SLMs) on the microtasks around a frontier large language model (LLM) planner: auto-approving shell commands, writing memory, selecting tools, ranking past turns. We ask…

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衡量微任务资格差距：现成的SLM何时足以用于Agent Harness？

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