A minimal hackable implementation of policy gradients (GRPO, PPO, REINFORCE) (github.com)

A new GitHub repository has unveiled a minimal and hackable implementation of popular policy gradient algorithms: Group Relative Policy Optimization (GRPO), Proximal Policy Optimization (PPO), and REINFORCE. Unlike traditional RL libraries that often are complex and difficult to debug, this project is built using PyTorch with a focus on educational accessibility. It allows users to train policy models on a single GPU, making it easier for researchers and developers to explore reinforcement learning concepts without the hurdles of distributed systems. This repository streamlines the inner workings of policy gradient methods, featuring a simplified structure comprising key components such as training loops, loss functions, and replay buffers. It supports several algorithms with specific YAML configuration files, enabling quick adjustments for various tasks, including a novel dataset generation approach using Reasoning Gym. The project's emphasis on clarity and simplicity makes it an invaluable resource for those new to RL, while still offering flexibility for more experienced practitioners looking to delve into advanced reinforcement learning techniques.