Human intelligence excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive intelligent models, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research platform designed for multi-step, real-world tasks. Specifically, OpenAGI uses a dual strategy, integrating standard benchmark tasks for benchmarking and evaluation, and open-ended tasks including more expandable models for creative problem-solving. Tasks are presented as natural language queries to the LLM, which then selects and executes appropriate models. We also propose a Reinforcement Learning from Task Feedback (RLTF) mechanism that uses task results to improve the LLM's ability, which creates a self-improving AI feedback loop. While we acknowledge that AGI is a broad and multifaceted research challenge with no singularly defined solution path, the integration of LLMs with domain-specific expert models, inspired by mirroring the blend of general and specialized intelligence in humans, offers a promising approach towards AGI. We are open-sourcing the OpenAGI project's code, dataset, benchmarks, evaluation methods, and demo to foster community involvement in AGI advancement: https://github.com/agiresearch/OpenAGI.

翻译：人类智能擅长通过组合基本技能来解决复杂任务。这一能力对人工智能至关重要，应被嵌入全面的智能模型中，使其能够利用专家模型解决复杂任务，进而迈向通用人工智能。大语言模型展现出强大的学习与推理能力，并能有效调用外部模型处理复杂问题。本文介绍了OpenAGI——一个面向多步现实任务的开源通用人工智能研究平台。具体而言，OpenAGI采用双重策略：整合标准基准任务用于评测与评估，以及包含更多可扩展模型的开放式任务用于创造性问题解决。任务以自然语言查询形式输入大语言模型，由模型选择并执行相应专家模型。我们还提出了一种任务反馈强化学习机制，利用任务结果提升大语言模型能力，形成自我改进的人工智能反馈循环。尽管我们承认通用人工智能是一个宽泛且多面的研究挑战，不存在单一解决方案路径，但受人类通用智能与专业智能融合的启发，将大语言模型与领域专家模型相结合，为迈向通用人工智能提供了一条有前景的路径。我们已开源OpenAGI项目的代码、数据集、基准测试、评估方法及演示，以推动社区参与通用人工智能发展：https://github.com/agiresearch/OpenAGI。