Equation 33 · Part 11 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared
≤
≤
What this part means
Less than or equal to.
Its job in the formula
Less than or equal to.
Full expression→≤→Article meaning
The passage around this formula
Set side by side, the four strategies do not compete on a single scale, and a comparison that reduces them to one leaderboard number is not describing what any of them actually trades off. Each holds a different quantity fixed as “already spent” and treats a different quantity as the one still to be paid: . Compression pays mostly , 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 and in full, with no discount, in exchange for a model that inherits nothing it was not deliberately given.…
Learn the underlying idea
An inequality compares values without claiming they are equal. It describes a range, threshold, or bound that a quantity may satisfy.
Open the illustrated inequalities: bounds and allowed ranges guide →
Sources cited in the surrounding passage
- [3] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning ↗
- [7] Neural Architecture Search with Reinforcement Learning ↗
- [10] Once for All: Train One Network and Specialize it for Efficient Deployment ↗
These citations provide research context; check each source for the exact claim it supports.