LDLMIn construction

Size
306M trained
Published
Meshchaninov et al. · May 2026
Line
Diffusion language model
View
Architecture
Source

LDLM @ e18c42a, not yet public · arXiv:2605.07933v1 · OpenWebText · 1024 · checkpoint, tokenizer and saved statistics not verifiedHow to read

Attention over all positions

Sampler › Denoiser › 12 denoiser blocks › Self-attention › Attention over all positions

Keys

×

supplied · no parameters

A rotated 64-feature key for every head and position.

shape
KK [B batch, 12 heads, T positions, 64 head features]
Sources

Contextual evidence from Rotate keys

The rotary table: angles from positions and inverse frequencies of base 10,000, computed in float32; queries and keys are rotated and values take the identity.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Rotate keys

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 Attention over all positions

Diffusion: self-conditioning on, `t_min` 0.05 and `eps` 10⁻⁵; the adaptive time sampler has 100 bins, a minimum weight of 0.0001, an EMA of 0.9 and updates every 5,000 steps; the denoiser’s EMA decay is 0.9999; scheduler `tanh` with d = 3, solver `euler`, `ode_sampling` false, N = 128 by default; the denoiser has 12 blocks of width 768 with 12 heads, time conditioning `adaln`, a standard MLP, softcap 50 and self-conditioning by concatenation; the timestep embedding’s `max_period` is 10.

The implementation, pinned at revision e18c42acdfb7, not yet public