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Equation 9 · Part 11 · Adapters, Native Pretraining, and Unified Tokens: The Main Multimodal Architectures, Compared

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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\}
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What this part means

A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.

Its job in the formula

A raised mark can be a power or an index. Its position and the surrounding notation determine which.

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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Learn the underlying idea

An exponent tells how a base is used in multiplication. In x³, x is the base and 3 is the exponent: x³ = x × x × x.

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Sources cited in the surrounding passage

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