# Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition

arXiv: https://arxiv.org/abs/2603.27695 · HTML: https://mlsanity.com/paper/2603.27695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, 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.

## Placed in

- Deep reinforcement learning methods / [Interpretable and logic-guided RL](https://mlsanity.com/deeprl/interpretable-and-logic-guided-rl/)

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation](https://mlsanity.com/paper/2408.04394/) (2024)
- [Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs](https://mlsanity.com/paper/2503.08551/) (2025)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [ConQuer: A Framework for Concept-Based Quiz Generation](https://mlsanity.com/paper/2503.14662/) (2025)
- [Reinforcement Learning Guided Multi-Objective Exam Paper Generation](https://mlsanity.com/paper/2303.01042/) (2023)

## 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.