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Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition

2026-03-29 · Ricardo Pedro Querido Andrade Silva, Nassim Bouarour, Dina Fettache, Sarab Boussouar, Noha Ibrahim, Sihem Amer-Yahia arxiv

Quiz design is a tedious process that teachers undertake to evaluate the acquisition of knowledge by students. Our goal in this paper is to automate quiz composition from a set of multiple choice questions (MCQs). We formalize a generic sequential decision-making problem with the goal of training an agent to compose a quiz that meets the desired topic coverage and difficulty levels. We investigate DQN, SARSA and A2C/A3C, three reinforcement learning solutions to solve our problem. We run extensive experiments on synthetic and real datasets that study the ability of RL to land on the best quiz. Our results reveal subtle differences in agent behavior and in transfer learning with different data distributions and teacher goals. This was supported by our user study, paving the way for automating various teachers' pedagogical goals.

📄 PDF Abstract BibTeX arXiv:2603.27695

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Reinforcement Learning Transfer Learning Topic coverage

Do LLMs and Humans Find the Same Questions Difficult? A Case Study on Japanese Quiz Answering

2025-11-15 · Naoya Sugiura, Kosuke Yamada, Yasuhiro Ogawa, Katsuhiko Toyama 외 arxiv

LLMs have achieved performance that surpasses humans in many NLP tasks. However, it remains unclear whether problems that are difficult for humans are also difficult for LLMs. This study investigates how the difficulty o…

CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning

2026-01-30 · Ji Shi, Peiming Guo, Meishan Zhang, Miao Zhang 외 arxiv

Code verifiers play a critical role in post-verification for LLM-based code generation, yet existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency. While rein…

Reinforcement Learning Code Generation

Scalable Online Exploration via Coverability

2024-03-11 · Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy

Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives -- policy optimization objectives that enable dow…

Efficient Exploration Q-Learning reinforcement-learning Reinforcement Learning

CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning

2025-12-21 · Zijun Gao, Zhikun Xu, Xiao Ye, Ben Zhou arxiv

Large language models (LLMs) often solve challenging math exercises yet fail to apply the concept right when the problem requires genuine understanding. Popular Reinforcement Learning with Verifiable Rewards (RLVR) pipel…

Reinforcement Learning Mathematical Reasoning

Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education

2023-01-30 · George Boateng, Victor Kumbol, Elsie Effah Kaufmann

There is a lack of enough qualified teachers across Africa which hampers efforts to provide adequate learning support such as educational question answering (EQA) to students. An AI system that can enable students to ask…

Math Position Question Answering