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Trustworthiness of generative language models (GLMs) is crucial in their deployment to critical decision making systems.

This assumption is problematic, particularly in complex data environments.

Heated chains more readily traverse valleys in the probability landscape topropose movestofar-awaypeaks,whilethecolderchainsmakethe local steps that explore the current peak or patch.

For out of distribution (OOD) inference, it is desired that the model can assign high epistemic uncertainty to the OOD regions compared to their ID counterparts. A.2 Policy Gradient based Reward Maximization for Segmentation Backbone This approach enables us to efficiently achieve the optimal solution for reward maximization. We present some examples of generated OOD examples in Figure 1(a). The results are presented in Figure 1(b)-(d). In Table 1, we present the results of our uncertainty estimation framework when applied to the Cityscapes dataset.