The free energy principle said an agent minimizes surprise two ways: change the model (perceive) or change the world (act). But a passive minimizer waits for surprise to arrive. Curiosity inverts the sign: the agent is rewarded for going where its model is wrong, it seeks the reflected wave on purpose, because a large mismatch is exactly where the information is. This is not a metaphor. A free-energy robot from OIST (Tinker, Doya & Tani, Science Advances 2026) rewards its policy by −D_KL[q‖p], the complexity term of the very free energy from the last lesson, and so is paid to seek observations that force its beliefs to update. It learns to ground language (verb–adjective–object commands) in roughly half the training epochs of a non-curious twin, and, tellingly, reproduces a signature of a developing agent rather than a tuned one: the U-shaped error curve of a toddler who says 'goed' before mastering 'went'. It develops; it is not programmed. Now the part, which you will measure yourself. Curiosity's speed advantage is not free and not universal, it scales with how concentrated the informative experience is. When the hard part of the world is a narrow band inside an easy majority, hunting it roughly halves the learning. When difficulty is spread evenly, plain uniform sampling already meets it and the edge shrinks; on a companion fabric-scale task where every case is moderately hard, it vanished to about 1×. What is NOT conditional is the other half of the story: the surprise the agent chases is the exact quantity that mastery drives to near-zero. One scalar, two phases, seek the reflected wave, then null it. That nulling is the matched interface, learned from the inside. One last turn, in the third cell: the surprise we weighted by above is prediction error, and that is only a proxy. OIST's actual reward is the information-gain (KL) term, how much a new observation would update the agent's beliefs. On a task with irreducible observation noise the two diverge sharply: prediction-error curiosity is captured by the noise (the classic 'noisy-TV' problem) and does worse than random, while information-gain curiosity samples the noisy region once, sees its beliefs stop changing, and moves on. The KL term is the noise-robust form, the one that scales to learning a world model.