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MDLM Diffusion — val_var_bpb 0.9901, EOS learning + full dataset shard rotation, 33M params, 1x AWS A10G#1241

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MDLM Diffusion — val_var_bpb 0.9901, EOS learning + full dataset shard rotation, 33M params, 1x AWS A10G#1241
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@aiejvn aiejvn commented Apr 2, 2026

Builds on PR #1106 (MDLM stack). Two additions:

EOS learning: Token 1 (<s>) is used as a document boundary anchor — never masked during diffusion. A dedicated PAD_ID=1025 (separate from MASK_ID=1024) fills post-EOS positions and is excluded from the loss, preventing collision between structural padding and diffusion masking.

Shard rotation: ShardedDataLoader loads N shards at a time and rotates between groups across training, enabling full FineWeb 10B training without loading the entire dataset into RAM. Explicit memory freeing between groups; shards loaded one-at-a-time into a pre-allocated buffer to avoid 2× peak allocation.

Ablation finding: Val BPB is flat across attention head counts {2, 4, 8, 16, 32} at fixed model dim — head count appears invariant for bidirectional diffusion LMs.

Non-record reason: Trained on 1× AWS A10G (1267 min). Requires 8×H100 SXM for wall-clock compliance.

Model BPB
This (MDLM v5) 0.9901
PR #1106 (prior best diffusion) 1.1465
AR baseline 1.2244

@aiejvn aiejvn changed the title Non-record: MDLM Diffusion — val_var_bpb 0.9901, EOS learning + full dataset shard rotation, 33M params, 1x AWS A10G MDLM Diffusion — val_var_bpb 0.9901, EOS learning + full dataset shard rotation, 33M params, 1x AWS A10G Apr 2, 2026
HateBunnyPlzzz added a commit to Itssshikhar/parameter-golf that referenced this pull request Apr 2, 2026
Approaches revamped (old eval-only approaches removed):
- 01: Low-Rank Factored MLP (18 layers in 16MB via rank-128 MLP factors)
- 02: Reptile Meta-Learning Warmdown (meta-optimize for TTT adaptability)
- 03: SVD + Quantized Factors (13 layers via spectral compression)
- 04: Multi-Token Prediction + BPB-Weighted Loss (training loss innovation)
- 05: Gram-Newton-Schulz + FP8 Training (30% more steps in 10 min)

Unmerged PR research saved to unmerged_runs/:
- PR openai#1263: SLOT (0.9354 BPB, legality contested)
- PR openai#1246: Trinity Ternary (0.9650 BPB)
- PR openai#1241: MDLM Diffusion (0.9901 BPB)
- PR openai#1252: WARP (1.0713 BPP)
- PR openai#1257: Complement Training (1.0855 BPB)
- PR openai#1274: Parallel Residuals + Depth Recurrence (1.0876 BPB)
- PR openai#1260: MuonEq-R + Depth Recurrence (1.0929 BPB)
- PR openai#1254: XSA + LoRA TTT (1.1070 BPB)

Key finding: without eval tricks, frontier is ~1.09 BPB (PR openai#1260)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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