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Equation 9 · Part 1 · Adapters, Native Pretraining, and Unified Tokens: The Main Multimodal Architectures, Compared

Symbol L_seq

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\}
Lseq\mathcal{L}_{\text{seq}}

What this part means

LsL_seq is part of the quantity the equation computes from the expression on the right.

Its job in the formula

LsL_seq is part of the quantity the equation computes from the expression on the right.

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 subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

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Sources cited in the surrounding passage

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