Cerebras

This post introduces the application of online Reinforcement Learning (RL), specifically GRPO, to enhance LLM reasoning and planning capabilities. It demonstrates this by teaching LLMs to solve Sudoku puzzles, exploring various puzzle representations (Markdown, Row, Grid) and guidance levels. The work details training methodologies, including dynamic sampling and priority-based sampling for curriculum learning, and analyzes the impact on metrics like avg@4 and pass@4. It also investigates the generalization of trained models to different representations and public benchmarks (AIME, GPQA), showing improved performance in complex task handling.