TLDR Tech / Practical
Scalable asynchronous RL maintains fixed off-policy gap
A scaling strategy for asynchronous reinforcement learning is proposed, designed to preserve a constant off-policy gap as batch sizes increase. The approach combines a minimum engine-mismatch value with a staleness factor dependent on token age and update variability. By synchronizing the update process and token queue on a single data clock, the framework provides guidelines for adjusting learning rates and queue sizes across different optimization methods.
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