学习信任经验：一种监控-信任-调节框架用于不可观测反馈可靠性下的学习
Learning to Trust Experience: A Monitor-Trust-Regulator Framework for Learning under Unobservable Feedback Reliability
AI总结：
本文提出了一种监控-信任-调节框架，通过自我诊断提升在不可观测反馈可靠性下的学习可靠性。
摘要：
在不可观测反馈可靠性下学习提出了一个独特的挑战，超越了优化鲁棒性：系统必须决定是否从经验中学习，而不仅仅是如何稳定地学习。我们将此设置称为在不可观测可靠性下的知识可识别性（EIUR），其中每个经验都有一个潜在的可信度，可靠和不可靠的反馈可以局部无法区分，数据由学习者自身不断演变的信念和行动在闭环中生成。在EIUR中，标准鲁棒学习可以稳定收敛，但会形成高自信的系统性错误信念。我们提出元认知调节作为实际回应：一个第二、反思性的控制循环，通过学习者内部动态中的内生证据推断经验的可信度。我们将此形式化为一个模块化的监控-信任-调节（MTR）分解，并通过自我诊断实现。自我诊断维护一个缓慢变化的经验信任变量，通过软调节学习更新，而无需外源可靠性标签或显式的腐败模型。经验上，在此处研究的EIUR环境中，自我诊断与改进的知识可识别性相关。在强化学习中，它使能够实现校准的怀疑并在系统性腐败奖励下恢复。在监督学习中，它揭示了一个关键的分离：性能恢复并不意味着知识恢复。准确性可以反弹，而内部信念动态仍可能被早期误导数据锁定，这种失败只能通过反思性诊断检测到。共同，MTR和自我诊断为在不可观测可靠性下的自主学习中的内在可靠性评估提供了组织抽象和具体的设计模板。
英文摘要：
Learning under unobservable feedback reliability poses a distinct challenge beyond optimization robustness: a system must decide whether to learn from an experience, not only how to learn stably. We study this setting as Epistemic Identifiability under Unobservable Reliability (EIUR), where each experience has a latent credibility, reliable and unreliable feedback can be locally indistinguishable, and data are generated in a closed loop by the learner's own evolving beliefs and actions. In EIUR, standard robust learning can converge stably yet form high-confidence, systematically wrong beliefs. We propose metacognitive regulation as a practical response: a second, introspective control loop that infers experience credibility from endogenous evidence in the learner's internal dynamics. We formalize this as a modular Monitor-Trust-Regulator (MTR) decomposition and instantiate it with self-diagnosis, which maintains a slowly varying experience-trust variable that softly modulates learning updates, without exogenous reliability labels or an explicit corruption model. Empirically, in the EIUR regimes studied here, self-diagnosis is associated with improved epistemic identifiability. In reinforcement learning, it enables calibrated skepticism and recovery under systematically corrupted rewards. In supervised learning, it exposes a critical dissociation: performance recovery does not imply epistemic recovery. Accuracy can rebound while internal belief dynamics remain locked-in by early misleading data, a failure detectable only through introspective diagnostics. Together, MTR and self-diagnosis provide an organizing abstraction and a concrete design template for intrinsic reliability assessment in autonomous learning under unobservable reliability.
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