Unsupervised Environment Design (UED) formalizes the problem of autocur-ricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student's ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to

Learning from noisy data is a fundamental problem in many machine learning applications.

Instructional "how-to" videos online allow users to master new skills and everyday DIY tasks, from

Specifically, the latter two graphs are first disentangled from the interaction graph.

Large language models (LLMs) are considered a crucial technology for advancing intelligent education since they exhibit the potential for an in-depth understanding of teaching scenarios and providing students with personalized guidance. Nonetheless, current LLM-based application in personalized teaching predominantly follows a "Question-Answering" paradigm, where students are passively provided with answers and explanations. In this paper, we propose SocraticLM, which achieves a Socratic "Thought-Provoking" teaching paradigm that fulfills the role of a real classroom teacher in actively engaging students in the thought

Currently, students are identified as needing additional educational support using a'wait-to-fail' approach, i.e., waiting until a child has not made expected gains in reading before there is a reevaluation of their instructional needs.

Educational recommendation lies in constructing a recommend system that can process the interactions between students and questions.