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torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools and algorithms for the entire ML lifecycle, including supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), data preprocessing, model evaluation, and building robust production-ready pipelines. Ideal for data scientists needing an integrated solution for complex data analysis.

Stable Baselines3 is a PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms, including PPO, SAC, DQN, and TD3. It offers a scikit-learn-like API for easily training RL agents, developing custom Gymnasium environments, and managing complex training workflows. Ideal for standard RL research, rapid prototyping, and advanced model optimization.