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English(EN) ActICL: Post-Training for Agentic Multimodal In-Context Learning

AI模型采用新的智能体中期训练和上下文学习技术进行训练

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的研究论文探讨了AI模型的高级训练技术。第一篇论文介绍了一种开放的中间训练方法，旨在为智能体训练后的基础模型进行预训练，并在Nemotron模型的软件工程基准测试中取得了显著改进。第二篇论文ActICL提出了一种智能体多模态上下文学习的方法，其中AI智能体学习逐步检索和优化演示以增强其推理和回答能力，在包括GPT 5.6 "Sol"在内的各种基准测试和模型上显示出显著的性能提升。 AI

影响 这些中期训练和上下文学习方面的进步可能带来更强大、更具智能体的AI系统，特别是在软件工程和多模态推理等复杂领域。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了AI模型的新颖训练方法。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI模型采用新的智能体中期训练和上下文学习技术进行训练

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两篇在arXiv上发表的学术论文，详细介绍了AI模型的新颖训练方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Soumye Singhal, Joseph Jennings, Ka Shun Shum, Alexander Bukharin, Khushi Bhardwaj, Zirui Liu, Venkat Srinivasan, Jonathan Li, Yash Jain, Kezhi Kong, Markus Kliegl, Olivier Delalleau, Sanjeev Satheesh, Mostofa Patwary ·

    为Agentic训练后预训练基础模型：一个开放的中间训练配方

    arXiv:2610.22019v1 Announce Type: cross Abstract: Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stage is built. We present an open midtraining recipe …

  2. arXiv cs.CL TIER_1 English(EN) · Yanshu Li, Ruixiang Tang, Junyi Jessy Li, Elias Stengel-Eskin ·

    ActICL：面向Agentic多模态上下文学习的训练后方法

    arXiv:2610.19475v1 Announce Type: cross Abstract: Multimodal in-context learning (ICL) is an important capability of multimodal large language models (MLLMs). Its performance is closely tied to the demonstrations it uses, making demonstration retrieval a core problem for ICL. Com…

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