TEACHING LANGUAGE MODELS TO SOLVE SUDOKU THROUGH REINFORCEMENT LEARNING

Summary from the TLDR AI newsletter

This research explores teaching AI language models to solve Sudoku puzzles using reinforcement learning, specifically employing Group Relative Policy Optimization (GRPO) on models like Qwen 2.5, without relying on external data or larger model distillation. A multi-faceted reward system was designed, emphasizing correct formatting of the answer, proper grid structure, and accurate solution, to guide the models in learning the logical rules and spatial reasoning needed for Sudoku, transforming them from text predictors to structured problem-solvers.