Author(s): Dr Swarnendu AI

Originally published on Towards AI.

Starting Point (The Observation)

You compute attention weights using softmax:

The article explains how temperature modifies softmax by dividing the exponent input, changing the concentration of attention weights from peaky (low T) to nearly uniform (high T). It then derives the underlying mechanism using ratios of exponentials, showing that temperature controls how sensitive the distribution is to score differences (effectively flattening or sharpening the exponential curve). The discussion connects these changes to entropy, gradient flow (via a 1/T factor), and learning dynamics: low temperature encourages selection and sparse gradients, while high temperature improves training stability and smooths the optimization landscape but reduces selectivity. It also covers numerical stability issues (overflow in exp(s/T)) and how practical implementations use stability tricks, plus the broader implications for attention behavior, implicit regularization, optimization landscape curvature, and why the common scaling in transformers (e.g., dividing by √d_k) effectively serves as an implicit temperature choice. The conclusion emphasizes that temperature is a single scalar lever that fundamentally shapes probability distributions, gradients, stability, optimization, and what the model learns to attend to.

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Published via Towards AI