AI 自己出题自己考，成绩反而更好
现在的 AI 训练，题目是人事先出好的，练到后面就瓶颈了。这篇让 AI 自己给自己出题：一个模型分饰两角，一个负责写训练环境(相当于出题)，一个负责解题，出题的那个会盯着解题的那个哪里不会，专挑薄弱点出题，但又不至于完全做不出。结果在数学、科学、代码等 8 个测试集上平均提升 5.3 分，工具调用场景提升更明显。这不是你明天能用的功能，但它指向一个方向：AI 的进步可能不再依赖人类喂题，而是自己给自己制造挑战。
📄 原文摘要(英文)
Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) keep the goal distribution fixed as the learner scales. 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 use. The Reasoning Agent's regret is estimated using the gap between its reward with and without privileged hints; in optimizing this regret signal the Environment Designer learns to target environments at the edge of the agent's capabilities while keeping them feasible. Through extensive experimentation, we find several components critical to success: grounding the Environment Designer on documents sampled from a large pretraining corpus, and giving it an accumulated environment memory. Scaling to 30B-parameter models, SPADE improves over the strongest fixed-environment baseline by +5.3 on average across eight held-out math, science, code, and reasoning benchmarks, and lifts the tool-use setting by +5.7 on BFCL-v4 multi-turn and +13.9 on ACEBench-Agent; on the games setting, the margin over the strongest baseline grows with model scale. By making environment design itself a learnable component, SPADE takes a concrete step toward open-ended self-improvement.