Equation 33 · Part 5 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared

Symbol C_search

C_{\text{lifetime}} = \underbrace{C_{\text{reuse}}}_{\text{compress}} \;+\; \underbrace{C_{\text{curate}} + C_{\text{train}}}_{\text{train small}} \;+\; \underbrace{C_{\text{search}}}_{\text{NAS}} \;+\; Q \cdot \bar c_{\text{tok}}, \qquad \text{with} \quad M_{\text{resident}} \le B_{\text{mem}}

C_{\text{search}}

What this part means

the architecture search pays.

Its job in the formula

$C_s$ earch is one of the signed contributions combined to compute the quantity on the left.

Full expressionSymbol C_searchArticle meaning

Where the article explains it

Architecture search pays $C_{\text{search}}$ , which can be enormous when paid fresh per target [ 7 ] or amortized across many targets when paid once as a supernet [ 10 ] , on top of whatever strategy trains the architecture it discovers.

The passage around this formula

…in the first place. Training small on purpose pays $C_{\text{curate}}$ and $C_{\text{train}}$ in full, with no discount, in exchange for a model that inherits nothing it was not deliberately given. Architecture search pays $C_{\text{search}}$ , which can be enormous when paid fresh per target [ 7 ] or amortized across many targets when paid once as a supernet [ 10 ] , on top of whatever strategy trains the architecture it discovers. Sparse mixture-of-experts is the odd one out in this accounting: its saving shows up…

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Sources cited in the surrounding passage

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