Why bigger language models often win — and a simple trick to train them smarter
Researchers found a clear and predictable rule for how well language models learn. As you give a model more size, more data, or more computing power, its performance improves in a smooth way. This pattern holds across a huge range of scales, which is kinda surprising and helpful. Tweaks like changing layer depth or width usually change little, so the big drivers are size, data, and compute — not small design tricks. It turns out bigger models get more from each example, so they’re more bigger and more efficient with data than small ones. With a fixed budget you can get more by building a very large model, training it on a modest amount of data, and stopping before it fully converges. …
Why bigger language models often win — and a simple trick to train them smarter
Researchers found a clear and predictable rule for how well language models learn. As you give a model more size, more data, or more computing power, its performance improves in a smooth way. This pattern holds across a huge range of scales, which is kinda surprising and helpful. Tweaks like changing layer depth or width usually change little, so the big drivers are size, data, and compute — not small design tricks. It turns out bigger models get more from each example, so they’re more bigger and more efficient with data than small ones. With a fixed budget you can get more by building a very large model, training it on a modest amount of data, and stopping before it fully converges. That strategy saves time and cost, yet still makes smart results. The idea is simple: use scale wisely, not wastefully, and you often end up with better, cheaper outcomes — even when you thinks it shouldn’t work.
Read article comprehensive review in Paperium.net: Scaling Laws for Neural Language Models
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