Summary

val_var_bpb: 1.1465 (512 eval steps) | ~33M params | 1x NVIDIA GB10 (Project DIGITS)

First discrete diffusion model to beat the AR baseline (1.22 BPB) in parameter-golf. Beats previous best diffusion (PR #820, 1.625 BPB) by 0.47 BPB.

Results

Approach

• MDLM (Sahoo et al. 2024) masked diffusion with log-linear noise schedule
• 11L 512d bidirectional transformer with adaLN timestep conditioning
• Frozen visible-token logits in substitution parameterization
• Antithetic time sampling, ReLU^2 activation, RoPE
• Proper discrete absorbing-mask ELBO evaluation (not MC sampling)
• 6000 steps, AdamW, cosine warmdown

Key Findings (27 hyperparameter experiments)

• Masking eps=0.1 >> 0.001: biggest single improvement for diffusion LMs
• Wider > deeper at same param count (8L 640d > 14L 384d)
• AR tricks that don't transfer: LeakyReLU^2, BigramHash, prefix conditioning
• Eval method is critical: MC ELBO gave 2.41 BPB, discrete ELBO gave 1.15 on same model

Non-Record Reason

Trained on 1x NVIDIA GB10 (Project DIGITS), not 8xH100 SXM.

Test plan

• Reproduce on 8xH100 SXM within 10-minute budget
• Verify discrete ELBO with exact byte counting (currently uses ~4.3 bytes/token approximation)
• Compare with official evaluation harness

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