RLC 2026 Outstanding Paper: Mining 5TB Games for High-Value Chess Puzzles via Offline RL
allenainie · x · 2026-08-18
This post highlights the RLC 2026 Outstanding Paper Award-winning work. The researchers utilized an offline reinforcement learning system to process nearly 5TB of human gameplay data (one year's worth) and train a policy that recommends chess puzzles with high pedagogical value.
The team recruited International Masters and Grandmasters to evaluate the policy. Results showed that the recommended puzzles were both fun and challenging for the players, validating the effectiveness of offline RL in curating educational content.
Related event: Offline RL Mines 5TB of Chess Games to Find High-Value Puzzles(2 posts)→
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