← All parts of this equation

Equation 6 · Part 13 · Comparing the Main Approaches to Training Data and Synthetic Data

subscript

Ldistill=(1−λ) LCE(y, σ(zs))+λ T2 DKL(σ(zt/T) ∥ σ(zs/T))\mathcal{L}_{\mathrm{distill}} = (1-\lambda)\,\mathcal{L}_{\mathrm{CE}}(y,\, \sigma(z_s)) + \lambda\, T^2 \, D_{\mathrm{KL}}\big(\sigma(z_t/T) \,\|\, \sigma(z_s/T)\big)
subscript

What this part means

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

Its job in the formula

A subscript distinguishes a version, component, step, or member of a quantity. It does not automatically mean multiplication.

The passage around this formula

Distillation is the oldest of the five and the easiest to state precisely. A student model is trained not on hard labels but on a teacher’s softened output distribution, on the reasoning that the relative probabilities the teacher assigns to wrong answers carry information a one-hot label discards entirely [ 1 ] . Where logits are unavailable — the ordinary case when the teacher is a closed API — the same idea is applied at one remove: the teacher’s sampled text stands in for its distribution, and the student is trained on that text directly. Writing ztz_t and zsz_s for teacher and student logits, σ\sigma for the softmax, y for the hard label, and T for a softening temperature, the canonical…

Read this part in the article →

Learn the underlying idea

A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

Open the illustrated subscripts: which member of a family? guide →

Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.