Scalable In-Context Reinforcement Learning with Recurrent Algorithm Distillation
Recurrent Algorithm Distillation (RAD) matches the asymptotic performance of standard AD with significantly reduced context window sizes, offering a scalable solution for efficient in-context decision-making.
Yuan-Qing Ma, Zhen-Rui Zheng, Chen-Jun Xiao
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