Learn2Play Bench：LLM智能体在陌生环境中的经验学习能力如何？

中文摘要 AI 辅助

从经验中学习对于LLM智能体适应陌生和动态环境至关重要。因此，评估这种能力对于理解智能体如何有效地获取和使用新知识具有重要意义。现有基准试图评估这一能力，但它们主要评估那些规则已在指令中提供或预训练模型已熟悉的任务，这使得难以区分从交互中学习与利用现有知识进行推理。为了解决这一问题，我们引入了Learn2Play Bench，一个由新设计的基于文本的游戏组成的基准，其规则新颖或反直觉，要求智能体通过交互获取知识，而非仅依赖预训练知识。这些游戏提供可复现的反馈和自动评分，使得能够在多次尝试中对学习进行受控评估。我们还变化游戏实例，以测试智能体能否将所学应用于新情境。因此，我们评估了骨干模型、自进化方法和智能体框架对智能体学习能力的影响，揭示了三个发现：（1）经验保留：保留动作和反馈的完整记录比将这些经验总结为规则或策略更能支持有效学习。（2）人类智能体差距：表现最佳的人类玩家达到的峰值分数高于所评估的智能体。人类探索更多样化的策略，且重复动作更少。（3）框架重要：在骨干模型固定的情况下，更换框架可以在降低估计推理成本的同时提升性能。总之，这些发现为LLM智能体如何从经验中学习提供了见解，并为未来提高其学习能力的工作指明了方向。项目网站：此https URL

英文摘要

Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for understanding how effectively agents acquire and use new knowledge. Existing benchmarks have sought to evaluate this ability, but they primarily evaluate tasks whose rules are provided in the instructions or already familiar to pretrained models, making it difficult to distinguish learning from interactions from reasoning with existing knowledge. To address this, we introduce Learn2Play Bench, a benchmark of newly designed text-based games, whose rules are novel or counterintuitive, requiring agents to acquire knowledge through interaction rather than rely solely on pretrained knowledge. These games provide reproducible feedback and automatic scoring, enabling controlled evaluation of learning across repeated attempts. We also vary game instances to test whether agents can apply what they have learned to new situations. Therefore, we evaluate how backbone models, self-evolving methods, and agent harnesses affect agents' learning ability, revealing three findings: (1) Experience retention: Retaining complete records of actions and feedback can support more effective learning than summarizing these experiences into rules or strategies. (2) Human agent gap: Top-performing human players achieve higher peak scores than the evaluated agents. Human explore more varied strategies, and repeat actions less. (3) Harness matters: With the backbone fixed, changing the harness can improve performance while reducing estimated inference cost. Together, these findings provide insights into how LLM agents learn from experience and suggest directions for future work to improve their learning ability. Project website: https://liushiliushi.github.io/learn2play-bench-website/

发表机构

• National University of Singapore（新加坡国立大学）

机构由 AI 辅助整理，请以论文原文为准。