Want to improve your LLM?
Meta might have something that can help.
They recently released Pearl, a production-ready reinforcement learning agent.
It allows customization for various applications, making it adaptable to different real-world scenarios.
Pearl supports both online and offline policy learning, accommodating various data scenarios.
While skillfully balancing exploration and safety, it addresses the critical exploration-exploitation trade-off, upholding safety constraints.
At the heart of Pearl is its use of PyTorch, enhancing its capability for GPU-accelerated and distributed training.
The best part of all? It's open-source, fostering collaborative development and innovation within the AI community.
