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Equation 9 · Part 2 · Adapters, Native Pretraining, and Unified Tokens: The Main Multimodal Architectures, Compared

Symbol t

Lseq=−∑t=1Tlog⁡pθ ⁣(zt∣z<t),zt∈{0,1,…,V−1}\mathcal{L}_{\text{seq}} = -\sum_{t=1}^{T} \log p_\theta\!\left(z_t \mid z_{<t}\right), \qquad z_t \in \{0, 1, \dots, V-1\}
tt

What this part means

t appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

Its job in the formula

t appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

The passage around this formula

Meta’s Chameleon commits to the same principle at a much larger scale and is candid that stability, not capability, was the hard engineering problem. It describes itself as “a family of early-fusion token-based mixed-modal models” that interleaves image and text tokens in one sequence, generating either kind at any position, and its authors state directly that this “requires a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting” [ 7 ] . The paper documents the failure mode this addresses concretely: without a query-key normalisation step controlling the growth of attention logits,…

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A function assigns an output to each allowed input. The expression f(x) means “apply f to x”.

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Sources cited in the surrounding passage

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