Show HN: Multiturn GRPO on the DGX Spark (github.com)

A developer has debuted a new implementation of the Generalized Reinforcement Policy Optimization (GRPO) algorithm specifically tailored for the DGX Spark platform. This custom approach was motivated by challenges faced with existing reinforcement learning libraries when adapting them for unique environments. The initial experiment involves training IBM’s Granite 4.0 model, a 350 million parameter configuration, to play a simplified version of Blackjack, resulting in a peak win rate of approximately 41%. This initiative is significant for the AI/ML community as it democratizes access to reinforcement learning capabilities on powerful hardware like the DGX Spark, allowing researchers and developers to customize and experiment more freely. With thorough installation instructions provided for Ubuntu 24.04, the implementation encourages further exploration and adaptation in various tasks beyond the initial Blackjack game. By focusing on ease of use and hackability, this effort aims to inspire innovation and lead to new advancements in reinforcement learning methodologies.