LDLMIn construction

Meshchaninov et al. · May 2026Diffusion language model

MLP

Pinned implementation and paper LDLM @ e18c42a, not yet public · arXiv:2605.07933v1 · OpenWebText · 1024 · checkpoint, tokenizer and saved statistics not verified · How to read

Training Generation

x_mlp[B, T, 768]Expansion

h=x_{\mathrm{mlp}}W_{1}+b_{1}

h[B, T, 3072]GELU

g=\operatorname{GELU}(h)

g[B, T, 3072]Contraction

\Delta_{\mathrm{mlp}}=gW_{2}+b_{2}

Δ_mlp[B, T, 768]

Denoiser › 12 denoiser blocks › MLP

MLP input

tensor

The normalized and time-modulated features entering the MLP.

shape: $x_{\mathrm{mlp}}$ [B batch, T positions, 768 d]

Sources

Contextual evidence from Modulate

The residual update is scale · dropout(x) added to the residual; modulation is x · (1 + scale) + shift.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Modulate

A block normalizes, modulates by the time condition’s shift and scale, projects to queries, keys and values, rotates queries and keys, attends over all positions with softcap, and adds the projected output scaled by a gate; the MLP branch does the same with its own shift, scale and gate.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Expansion

The standard MLP: an affine layer, GELU with the tanh approximation, an affine layer.

The implementation, pinned at revision e18c42acdfb7, not yet public