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## Summary This chunk covers the critical pivot from diagnosing why v5 regressed to building and deploying a DDTree-optimized training pipeline (experiment-ddtree). The user flagged that v5's accuracy trajectory was worse than pre-fix runs despite three bug fixes (clean targets, 4-layer fc, hard CE). A deep investigation comparing our code line-by-line against the official vllm-project/speculators repository uncovered three additional fundamental bugs: the fully connected layer was using only 4 of 5 target layers instead of all 5 (official: `nn.Linear(5*H, H)`), target logits were computed from layer 61 instead of the actual model output at layer 63 (missing 2 layers of refinement), and the gamma default was 7.0 instead of the official 4.0. Fixing these in v6 produced dramatically better convergence—step 475 accuracy (0.14) matched v5's step 2400, with streak nearly double at the same point. The user then directed focus toward DDTree-specific optimizations. Three parallel research agents investigated diffusion LM training, distillation for drafters, and DDTree tree construction, converging on several high-impact changes. The experiment-ddtree branch was created implementing: gamma=10 (DDTree values later positions more), sliding window attention on layers 0-3 (matching z-lab's 4 SWA + 1 full attention pattern), uniform noise matching official speculators code, 15% soft KL blended with CE (teaching probability ordering for top-K coverage), CAP auxiliary confidence loss from LLaDA2.0 (sharpening confident predictions), block_size=32, and max_anchors=1024. A fused gradient-checkpointed loss function was required to avoid OOM at this scale—processing lm_head + loss in chunks with gradient checkpointing so the backward graph recomputes logits from tiny [chunk, 5120] tensors instead of storing [chunk, 248K] tensors across all chunks. The pipeline was also scaled to use 2 drafter GPUs (6+7) with weight averaging every 50 steps, and a per-device flex_attention compilation cache fixed a multi-GPU tracing conflict. The final configuration runs on all 8 GPUs (5 targets + 3 drafters) at ~17.5 Ktok/s with a ~7 day ETA for 6 epochs, each step covering 4x more training positions than the v6 baseline.

The Two-Phase Transformation: From Bug-Fixing to DDTree-Optimized Training in a Speculative Decoding Pipeline 3569 words

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