IsoFLOP curves for dependence of perplexity on log-data seem mostly symmetric (as in Figure 2 of Llama 3 report), so overtraining by 10x probably has about the same effect as undertraining by 10x. Starting with a compute optimal model, increasing its data 10x while decreasing its active parameters 3x (making it 30x overtrained, using 3x more compute) preserves perplexity (see Figure 1).
GPT-3 is a 3e23 FLOPs dense transformer with 175B parameters trained for 300B tokens (see Table D.1). If Chinchilla’s compute optimal 20 tokens/parameter is approximately correct for GPT-3, it’s 10x undertrained. Interpolating from the above 30x overtraining example, a compute optimal model needs about 1.5e23 FLOPs to get the same perplexity.
(The effect from undertraining of GPT-3 turns out to be quite small, reducing effective compute by only 2x. Probably wasn’t worth mentioning compared to everything else about it that’s different from GPT-4.)
IsoFLOP curves for dependence of perplexity on log-data seem mostly symmetric (as in Figure 2 of Llama 3 report), so overtraining by 10x probably has about the same effect as undertraining by 10x. Starting with a compute optimal model, increasing its data 10x while decreasing its active parameters 3x (making it 30x overtrained, using 3x more compute) preserves perplexity (see Figure 1).
GPT-3 is a 3e23 FLOPs dense transformer with 175B parameters trained for 300B tokens (see Table D.1). If Chinchilla’s compute optimal 20 tokens/parameter is approximately correct for GPT-3, it’s 10x undertrained. Interpolating from the above 30x overtraining example, a compute optimal model needs about 1.5e23 FLOPs to get the same perplexity.
(The effect from undertraining of GPT-3 turns out to be quite small, reducing effective compute by only 2x. Probably wasn’t worth mentioning compared to everything else about it that’s different from GPT-4.)