Llama 3.1

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405B

Published

Meta · Jul 2024

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Dense decoder · follows Llama 3

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The Llama 3 Herd of Models (arXiv 2407.21783v3) · DPO (arXiv 2305.18290v3) · no training code is publishedHow to read

Rejection sampling and SFT

x[B]Latest policy

{\htmlClass{vl-stream-k}{K}}\ \text{responses},\ {\htmlClass{vl-stream-k}{K}}\approx 10\text{–}30

Y[B, K, T]Reward model r[B, K]Keep the bestHighest score.y[B, T]Model P[B, T, V]Cross-entropy, prompt masked

\mathcal L_{\mathrm{SFT}}=-\sum_{t\in y}\log P(y_t\mid x,y_{<t})

ℒ_SFT[1]hatched band: not updated in this step · solid band: what this stage trainsthe reward model is trained beside this step, on the pre-trained checkpoint

Rejection sampling and SFT

Model

learned · not counted here

The model being fine-tuned: the paper says SFT fine-tunes the pre-trained language model.

in: $y$ [B batch, T positions]
out: $P$ [B batch, T positions, V]

Sources1

Direct evidence

§4.1.3: the reward model performs rejection sampling on human annotation prompts; with this data and other sources, including synthetic data, the pre-trained language model is fine-tuned with a standard cross-entropy loss on the target tokens, the loss masked on prompt tokens; learning rate 10⁻⁵ over 8.5K to 9K steps for the largest models.

sft · arXiv 2407.21783 / Llama3-Herd-2407.21783v3.pdf
Pinned revision v3 ↗ · p. 16