Asynchronous-Training

TL;DR for operators A distributed model-training system can keep every organization’s raw records on local infrastructure and still leave several operational problems unresolved. Model updates can expose information, privacy spending can accumulate across training rounds, encrypted updates still have to be aggregated efficiently, and clients working from older model versions can send updates that no longer deserve equal influence. ...

TL;DR for operators Asynchronous RLHF buys throughput by allowing rollout workers to continue generating completions while the learner updates the policy. The invoice arrives later: some rollouts were generated by a policy that the learner has already left behind. The paper’s useful contribution is not merely the familiar observation that stale data can destabilize training. It identifies two different speed limits.1 ...