mlsanityData to 13 Oct 2026

Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition

arXiv 2603.27695 ↗First posted 29 Mar 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 19 references that are in the map. First posted on arXiv 29 Mar 2026.

Builds on

8 of 19 references.

References · 8

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,221
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,223
  3. 2015Prioritized Experience Replaycited by 1,078
  4. 2024Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluationcited by 29
  5. 2025Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMscited by 14
  6. 2024Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applicationscited by 12
  7. 2025ConQuer: A Framework for Concept-Based Quiz Generationcited by 2
  8. 2023Reinforcement Learning Guided Multi-Objective Exam Paper Generationcited by 1

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