SPADE: Self-Play in Adaptive Synthetic Executable Environments
We introduce SPADE (Self-Play in Adaptive Synthetic Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that learns to act in them. Each is a stateful, multi-turn environment (state transitions, reward functions, and verification code), so one interface spans reasoning problems and multi-step agentic tool…
Review the primary source, validate the main result, and establish whether any listed-company transmission is direct.
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The full methodology, effect size, and limitations still require analyst review.
Company impact remains unverified until a direct economic transmission is established.