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Executes the entire reinforcement learning pipeline—simulation, model inference, and gradient updates—within a single process to eliminate synchronization bottlenecks.
Distinct from Single-Process Servers: Distinct from Single-Process Servers: specifically targets RL training loops, not general server architectures.
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PufferLib is a reinforcement learning framework built around high-speed environment simulation and automatic hyperparameter optimization. It is designed to accelerate the entire RL training pipeline by running simulations at near-native speed and enabling the training of tiny models to super-human performance within seconds. The framework achieves its speed through a single-process training loop that eliminates inter-process communication overhead, vectorized batched simulation for parallel environment execution, and compiled C extensions that offload performance-critical computations. It als
Executes simulation, model inference, and gradient updates in a single process to eliminate synchronization bottlenecks.