What does this equation mean?

Compression pays mostly $C_{\text{reuse}}$ , a documented small fraction of a from-scratch training cost [ 3 ] , but only if a suitable large model is available to reuse 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 only in $\bar c_{\text{tok}}$ , the cost per served token, and it does essentially nothing to relax the constraint $M_{\text{resident}}$ $\le$ $B_{\text{mem}}$ that the other three strategies are built specifically to satisfy [ 13 , 12 ] .