Direct optimization of initial states in state-space models (SSM) offers flexibility beyond traditional transformer models by allowing the creation of fixed-size databases that summarize past interactions, including conversation styles. This method simplifies handling historical data and enables targeted modifications to the model's performance without adjusting its weights. The RWKB project exemplifies this approach by exploring fine-tuning models on downstream tasks through alterations of the initial state, enhancing control over the model's behavior.

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