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GROW：将GRPO与状态-动作建模对齐以支持开放世界VLM代理GROW: Aligning GRPO with State-Action Modeling for Open-World VLM Agents

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

研究人员推出了GROW，这是一种创新的强化学习框架，旨在增强开放世界视觉-语言模型（VLM）代理。该框架通过将轨迹分解为状态-动作样本，允许在复杂环境中更有效地学习和互动。结果令人印象深刻，GROW在Minecraft的800多个任务中实现了最先进的性能，显著超越了依赖监督微调的传统方法。

Researchers have unveiled GROW, an innovative reinforcement learning framework designed to enhance open-world vision-language model (VLM) agents. This framework distinguishes itself by decomposing trajectories into state-action samples, which allows for more effective learning and interaction in complex environments. The results have been impressive, with GROW achieving state-of-the-art performance across more than 800 tasks in Minecraft, significantly outpacing traditional methods that depend on Supervised Fine-Tuning.

要点

  1. GROW框架通过状态-动作建模改善开放世界VLM代理。

    GROW framework improves open-world VLM agents through state-action modeling.

  2. 在800多个Minecraft任务中取得了最先进的结果。

    Achieved state-of-the-art results in over 800 Minecraft tasks.

  3. 超越了依赖监督微调的现有方法。

    Surpasses existing methods that rely on Supervised Fine-Tuning.

为何重要

GROW的发展代表了强化学习领域的重大进展，特别是在开放世界环境中的应用。这可能导致更强大的AI系统能够实时学习和适应，从而增强其在各个领域的实用性。

The development of GROW represents a significant advancement in the field of reinforcement learning, particularly for applications in open-world environments. This could lead to more capable AI systems that can learn and adapt in real-time, enhancing their utility across various domains.

按谁的角度看

产品经理

对于产品经理来说，GROW的能力可以为开发需要适应性和实时学习的更复杂的AI驱动产品提供参考，可能导致更具吸引力的用户体验。

For product managers, GROW's capabilities could inform the development of more sophisticated AI-driven products that require adaptability and real-time learning, potentially leading to more engaging user experiences.

开发者

开发人员和工程师可以利用GROW的状态-动作建模技术构建更高效和有效的AI系统，提高依赖动态环境中复杂决策的应用性能。

Developers and engineers can leverage GROW's state-action modeling techniques to build more efficient and effective AI systems, improving the performance of applications that rely on complex decision-making in dynamic environments.

学生与求职者

在AI和机器学习领域的学生和求职者应关注GROW，因为它突出了可能塑造未来AI开发工作机会和项目的前沿研究和方法。

Students and job-seekers in AI and machine learning fields should take note of GROW as it highlights cutting-edge research and methodologies that could shape future job opportunities and projects in AI development.

覆盖来源

来源标题与摘录保留原文，以便核对与追溯。

  • arXiv cs.LG

    GROW: Aligning GRPO with State-Action Modeling for Open-World VLM Agents

    Researchers introduced GROW, a new reinforcement learning framework that improves open-world vision-language model (VLM) agents by decomposing trajectories into state-action samples. This innovation has led to state-of-the-art performance in over 800 Minecraft tasks, surpassing existing methods reliant on Supervised Fine-Tuning.

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