# Sample Importance
While a low alignment score signals non-adherence to the instruction, a very high alignment score may indicate an *trivial* sample, implying minimal challenge for the existing multi-modal AI MemAgents in the network. To ensure that novel and more challenging data receives higher rewards, we incorporate a generalized logistic function to assign an *importance weight* to each sample:
$$
\mathcal{W}(\mathbf{x}, \mathbf{y}) = 1 - \frac{1}{1 + \exp(-k' (s(\mathbf{x}, \mathbf{y}) - \tau'))}
$$
Importance Score
where $$k'$$ controls the sensitivity to variations in the alignment score $$s(\mathbf{x}, \mathbf{y})$$, and $$\tau'$$ determines the inflection point at which the importance transitions from low to high. This function allows us to dynamically scale the reward based on the novelty and difficulty of each sample.
By assigning higher rewards to novel and challenging examples, the model's training process prioritizes contributions that promote the network’s growth and adaptability. This adaptive weighting mechanism, enabled by the logistic function, ensures that the users are incentivized to submit data that meaningfully extends the network’s AI MemAgents.
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