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SPADE: Self-Play in Adaptive Synthetic Executable Environments

47 upvotes · 19 AUG 2026 · Bo Liu, Simon Yu, Yiding Jiang et al.

This paper introduces SPADE, a self-play framework that enables language agents to learn from adaptive, self-generated environments, allowing them to improve continuously without fixed goal distributions. Practitioners might care because SPADE can lead to more robust and open-ended AI models.

self-playadaptive environmentslanguage modelsreinforcement learningopen-ended self-improvement