Nando de Freitas demonstrates emergent reward maximization from AI agent interactions using an imitation learner trained without scalar reward labels

Users express optimism about research on emergent reward maximization in AI because experiments show robustness to teacher noise and limited data, supporting future agents that learn continually via interaction with minimal engineering.

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