Toy Models of Initialisation Effects on RL Dynamics

Geodesic, an AI safety research team, shared work on LessWrong exploring how a model's early decisions during reinforcement learning can lock it into a particular behavioral path. When two behaviors are equally rewarded, whichever the model explores first tends to dominate subsequent learning — a phenomenon the researchers call rich-get-richer dynamics. Using toy mathematical models, they demonstrated that underspecified behaviors, such as emotional state, may persist based on initialization rather than genuine reward signal. The findings suggest the alignment properties established before RL training may be as important as the training process itself.

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