This project is a distributed training infrastructure designed for aligning large language models through reinforcement learning. It functions as an end-to-end engine for complex alignment tasks, including proximal policy optimization, direct preference optimization, and iterative self-play. By providing a unified framework for multi-turn interactions and tool-use scenarios, it enables the development of models capable of reasoning and external environment engagement.
The framework distinguishes itself through a decoupled architecture that separates model training from sample generation. This asynchronous design allows for continuous throughput by partitioning compute resources between actor, reference, and rollout models. It supports large-scale distributed execution across multi-node clusters, utilizing high-performance communication primitives to synchronize model states and aggregate losses while maintaining stability through advanced policy clipping and variance reduction techniques.
Beyond its core reinforcement learning capabilities, the system includes comprehensive infrastructure for data management, reward modeling, and performance optimization. It features modular interfaces for integrating custom tools and external reward servers, alongside built-in support for sequence parallelism, low-precision training, and hardware-specific acceleration. Observability is integrated throughout the pipeline, providing tools for profiling distributed tasks, monitoring policy divergence, and tracking GPU memory usage.
The project is implemented in Python and provides a containerized environment for deployment across diverse hardware architectures.
