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Stale rollouts can introduce a significant bias in learning rates, fundamentally altering the stability landscape of asynchronous RLHF systems.
Training updates that improve performance in LLMs can actually degrade inference quality鈥攗nless you use the new Monotonic Inference Policy Update framework.
An open-source ecosystem for agentic learning, complete with a trained agent and novel policy optimization, promises to accelerate research by providing a standardized, scalable platform.
