Unverified50% confidenceFactExact time
Non-stationarity points out that a harness's effectiveness changes dynamically as the policy learns.
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50%
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Long-term
Relevance
11/4/2026
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UnverifiedMethods such as experience replay, opponent modeling or policy regularization often assume that non-stationarity is bounded or predictable.73% similarUnverifiedContinual learning methods make a trade-off between stability and plasticity: being too conservative prevents effective learning of new knowledge, while being too aggressive leads to rapid loss of old knowledge.72% similarUnverified多智能体强化学习领域的非平稳性问题在多人世界模型中被放大，因为其他玩家的行为分布会随策略动态改变72% similarUnverifiedPrompt engineering includes methods such as few-shot learning, chain-of-thought, and role-setting, and its returns are limited by the stability of the underlying engineering pipeline.71% similarUnverifiedUnder continual learning, the policy may learn a large amount of evasion knowledge through online RL, with a perturbation magnitude far exceeding what control evaluation assumes, causing protocols that originally passed evaluation to lose their meaning.70% similar
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