Uncovering the Turing Test's Latent Signal: Indistinguishability as Reward

Can a model learn by trying to fool another model? We show that it can. Inspired by the Turing Test and its recent generalization to arbitrary distinguishers, we introduce a *domain-based teacher-Turing-Test*, in which a stronger teacher model serves as both the *target* of imitation and the *distinguisher*, and a weaker student model is trained to make the teacher attribute responses to itself. We use this self-attribution (or fooling) probability as a reinforcement learning reward, without ever providing the student ground-truth answers, nor requiring the teacher to generate responses. We test our approach independently in four domains with verifiable answers: CommonsenseQA (reasoning), GSM8K (mathematics), KodCode (programming), and Spider (SQL), training a Qwen2.5-1.5B student/imitator model using DeepSeek V4 Flash as teacher/distinguisher. Beyond improving at passing the Turing Test, the student improves on independent, held-out evaluations in all domains and runs. To probe *why* being mistaken for the teacher can improve capability, our analyses reveal that self-attribution is associated with correctness – substantially more than formatting changes – and that it rewards *partial* correctness. Our results demonstrate a new, simple teacher-student training paradigm with a private and/or high-cost teacher. They also suggest that indistinguishability need not merely measure behavior – it can also be a mechanism for learning.