arXiv 2610.08215 · cs.AI · 2026-10-06
Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?
Yibo Li, Jinhang Qiu, Zhi Zheng, Qianyun Guo, Jiaying Wu, Shuo Ji, Bryan Hooi
National University of Singapore
在最新榜 #80 2026-W41 第 7 名 本站收录于 2026-10-21
一句话要点
Learn2Play Bench 用规则新奇甚至反直觉的文字游戏，检验 Agent 能否从经验中学习。
Learn2Play Bench uses text games with novel or counterintuitive rules to test whether agents learn from experience.
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主题与学科
同主题：智能体
- Raven: The Harness of Harnesses for Composable Agentic Intelligence ▲748
- False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents ▲642
- BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business ▲490
- Omni-IO Skills: Harnessing Your Agent Omni-Native ▲485
- Player-1: Learning to Play from Synthetic Experience ▲348
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
要点由本站根据摘要自写，不转载摘要原文，请阅读原文。