AgentOdyssey：面向测试时持续学习智能体的开放式长程文本游戏生成

摘要

为了让智能体在测试时通过与世界的交互持续学习，它们必须能够有效探索、获取新世界知识与技能、保留相关情景经验，并在长期范围内进行规划。为评估测试时持续学习智能体的这些关键能力，我们引入了AgentOdyssey——一个新颖的评估框架，该框架通过程序化生成具有丰富实体、世界动态和长期任务的开放式文本游戏。关键在于，AgentOdyssey超越了机器学习中“测试时不进行学习”的传统假设，将智能体置于一个持续、长期的环境中，使得学习和推理在整个部署过程中交织进行。我们进一步提出了一种多层面评估方法，不仅衡量游戏进度，还提供关于世界知识获取、情景记忆、对象与动作探索、动作多样性以及模型成本的诊断测试。我们在生成的游戏中评估了多种智能体范式。实验结果表明，智能体在关键能力上存在显著局限，并揭示了影响其有效时间视野的因素。尽管性能随基础模型增强而提升，但即使是最优智能体也远低于人类表现，仍有巨大的改进空间。在智能体机制中，我们发现短期记忆对多种智能体范式有益，并且是智能体测试时训练的重要组成部分。

English

For agents to learn continuously from interaction with the world at test time, they must be able to explore effectively, acquire new world knowledge and skills, retain relevant episodic experiences, and plan over long horizons. To evaluate these key abilities of test-time continual learning agents, we introduce AgentOdyssey, a novel evaluation framework that procedurally generates open-ended text games with rich entities, world dynamics, and long-horizon tasks. Critically, AgentOdyssey goes beyond the conventional machine learning assumption that learning does not occur at test time by placing agents in a continuous, long-horizon setting that interleaves learning and inference throughout deployment. We further propose a multifaceted evaluation methodology that measures not only game progress but also offers diagnostic tests on world knowledge acquisition, episodic memory, object and action exploration, action diversity, and model cost. We evaluate diverse agent paradigms in the generated games. Our experimental results reveal critical limits in agents' key abilities, as well as factors that influence their meaningful horizon. Although performance scales with stronger base models, even the top agent remains far below human performance, leaving substantial headroom for improvement. Among agent mechanisms, we find that short-term memory benefits multiple agent paradigms and is an important component of agent test-time training.