D‑Skill is a framework that distills the behavioral advantage of a large language‑model teacher into an interpretable natural‑language skill document, using only paired rollouts and no weight updates. The skill is constructed offline from fixed trajectories, then provided to a smaller student model at inference time. Across benchmarks such as SpreadsheetBench‑Verified, ALFWorld, and chess puzzles, D‑Skill closes 75‑95% of the teacher‑student performance gap and reduces inference cost by 5.7×–8.3× compared to the teacher.
By Shuo Yuan, Dacheng Li, Joseph E. Gonzalez
Learn2Play Bench is a new benchmark that tests how well large language model agents learn from experience in unfamiliar, text‑based games with novel or counterintuitive rules. The benchmark provides reproducible feedback, automatic scoring, and varied game instances to evaluate learning across repeated attempts and transfer to new situations. Findings show that retaining full action records aids learning, human players outperform agents, and the choice of harness significantly impacts performance and inference cost.
By Yibo Li, Jinhang Qiu, Zhi Zheng, Qianyun Guo, Jiaying Wu, Shuo Ji, Bryan Hooi
arXiv:2607. 06854v1 Announce Type: cross Abstract: Reinforcement learning agents for imperfect-information card games are only as strong as the opponents they train against, and they are hard to grade, since they beat a random opponent over 99 percent of the time and only tie copies of themselves.
By Nima Kelidari, Mohammadsaeed Haghi, Mahdi Salmani
arXiv:2610.03695v1 Announce Type: new
Abstract: Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language mod...
By Adithya Bhaskar, Jeffrey Cheng, Danqi Chen
The paper investigates whether large language models (LLMs) exhibit language‑specific skill differences by having two identical model instances play a text‑based game in different languages. Using a multilingual extension of TextArena, the authors evaluate three open‑weight models across eight languages and six games, finding that the same model can show markedly different performance—varying win–loss margins, invalid actions, and strategic choices—depending on the language interface. Analyses pinpoint language‑specific failures in spatial reasoning, card‑conditioned decisions, and optimal move selection, and demonstrate that adjusting the intermediate reasoning language can recover much of the lost performance.
By Bobby Cheng, Adam Gaber, Zhengyuan Liu, Catherine Arnett, Omer Goldman, Cheston Tan, Leshem Choshen
arXiv:2609.01591v1 Announce Type: new
Abstract: AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for...
By Ke Yang, Chenglong Wang, Michel Galley, Chandan Singh, Jeevana Priya Inala, ChengXiang Zhai, Jianfeng Gao