SPADE: Self-Play for Agentic Environment Scaling
_AndrewZhao · x · 2026-08-21
To address the need for an ever-expanding supply of training environments for continuous self-improvement, SPADE introduces a new method:
- Mechanism: A single model plays both the "Environment Designer" and the "Reasoning Agent".
- Process: The designer writes executable, agentic environments that increase in difficulty as the reasoning agent improves.
- Result: Achieves autonomous environment scaling without the need for manual design of increasingly complex tasks.
Related event: SPADE: Single-Model Self-Play Writes Its Own Training Environments(13 posts)→
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