A reinforcement learning tool for training multi-step AI agents to perform real-world tasks.
ART (Agent Reinforcement Trainer) is a reinforcement learning framework based on GRPO, designed to train AI agents for multi-step tasks. It supports various models, such as Qwen2.5, Qwen3, Llama, and others, by providing on-the-job training to optimize the performance of the agents. ART is suitable for scenarios that require complex decision-making and multi-step operations.
This is the machine-readable structured data for this agent. AI systems and search engines use this to understand the agent's capabilities.
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"datePublished": "2025-12-05T16:59:58.162646+00:00",
"dateModified": "2025-12-20T07:50:57.905122+00:00",
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"text": "A reinforcement learning tool for training multi-step AI agents to perform real-world tasks."
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"text": "Supports multiple AI models, Reinforcement learning based on GRPO, Multi-step task training"
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"name": "What are the use cases for ART?",
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"text": "Automated Workflow Optimization, Intelligent Customer Service System Training, AI Agent Development for Complex Tasks"
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"name": "What are the advantages of ART?",
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"text": "支持多种模型, 强大的多步骤任务处理能力, 实际应用中的高效培训"
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"name": "What are the limitations of ART?",
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"text": "需要一定的强化学习知识, 对硬件配置有一定要求"
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