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In the study '1000 Layer Networks for Self-Supervised RL', researchers observed that goal-directed behavior in neural networks emerges abruptly as a critical transition once the network reaches a sufficient depth of hundreds to thousands of layers, even in environments with little to no explicit reward.

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