Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning

2026-08-14

AI Summary

This study addresses the limited pedagogical value and suboptimal recommendation strategies of automatically generated chess puzzles by applying offline reinforcement learning and policy evaluation to large-scale puzzle mining, leveraging 1.5 billion user interaction records. The proposed framework enables automated quantification of instructional value and optimization of personalized recommendations. Experimental results demonstrate that the system significantly enhances training outcomes for novice players with Elo ratings between 100 and 1000, effectively helping learners overcome performance plateaus. Furthermore, the generated content has received qualitative endorsement from domain experts. Collectively, this work establishes a novel paradigm for automated educational material generation within intelligent tutoring systems, bridging the gap between large-scale data utilization and pedagogically effective content delivery.

Abstract

Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain expertise and be very time-consuming. Pedagogical materials often need to train students to engage in different thinking patterns. In some domains, such as chess, puzzles are used to help students practice their skills in calculating the next moves and recognizing known patterns on a board. Giving students a practice set of puzzles to help them learn different modes of thinking is challenging because the teacher needs to carefully balance between different motifs and how many look-ahead steps a student needs to perform. Popular online platforms like Chess.com and Lichess offer players millions of puzzles. Unlike chess tactics puzzles procured by human experts, where chess beginners can learn valuable insights, these puzzles are automatically generated and often regarded as having low pedagogical value. These platforms also rely on a heuristic to recommend puzzles to users for practice. Using the user history data over an entire year, a total of 1.5 billion puzzle-solving histories, we learn the pedagogical value of a puzzle and how to automatically choose a set of puzzles to better support chess learners using insights from offline reinforcement learning. We show that using offline policy evaluation, our trained policy has significant impact on beginners with puzzle-solving Elo range of 100--1000, particularly for the group of beginners whose learning growth was stagnant. We also performed a qualitative analysis of the puzzles discovered by our model by collecting annotation ratings from expert chess players. The success of our pipeline shows promise for a future where we can understand the pedagogical values of practice items given general user interaction data.

Problem

• Chess Puzzles
• Pedagogical Value
• Offline Reinforcement Learning
• Personalized Learning

Innovation

• Offline Reinforcement Learning
• Pedagogical Value Modeling
• Offline Policy Evaluation
• Chess Puzzle Recommendation
• Educational Data Mining