The Moment of Abandonment: Killing v4 to Fix Three Critical Bugs

In the long arc of training a speculative decoding drafter for large language models, few moments are as consequential as the decision to abandon a running training run and start over. Message [msg 9149] captures precisely such a moment: a single bash command executed over SSH that kills the v4 DFlash training session, archives its checkpoints, and clears the workspace for a fundamentally corrected v5. On its surface, the message is unremarkable—a few lines of shell output confirming that checkpoints at steps 2000 and 4000 along with a training log have been moved to a new directory. But in the context of the conversation, this message represents the culmination of a deep investigative arc that uncovered three critical bugs in the training pipeline, and the disciplined decision to discard weeks of accumulated compute rather than continue down a fundamentally wrong path.

Why This Message Was Written

The message exists because the assistant and user had just completed a painstaking root-cause analysis of why their DFlash drafter was plateauing far below the performance of the z-lab reference model. The evaluation harness, built on the CT129 SGLang server, had revealed a stark 4x gap: at step 20,000, their model achieved a DDTree-8 acceptance rate of τ≈3.0 on fresh coding prompts, while the z-lab model achieved τ≈12.4. This was not a matter of training longer—it was an architectural and algorithmic mismatch.

The investigation, spanning messages [msg 9124] through [msg 9148], had traced the gap to three distinct bugs:

1. Noise corrupting target logits: The training pipeline applied Gaussian noise to the combined 5-layer hidden state tensor before extracting the last layer for target logit computation. This meant the model's training signal was itself corrupted by noise—the very noise that was supposed to regularize the drafter's conditioning was directly polluting the ground-truth targets.
2. FC shortcut including the target layer: The official DFlash architecture uses (N-1) layers as input to the fully-connected projection layer (fc), reserving the last layer exclusively for target logit computation. The implementation had been feeding all N layers to fc, creating a dangerous shortcut: the same information appeared in both the drafter's conditioning context and the loss target, allowing the model to "cheat" by copying information rather than learning to predict.
3. Loss function mismatch: The official DFlash paper uses pure hard cross-entropy loss with gamma=4.0. The implementation had been using a composite loss: 70% soft KL divergence (temperature 2.0) + 30% hard CE + streak-aware dynamic weighting + gamma=10. The soft KL component forced the drafter to match the full 248K-dimension vocabulary distribution instead of simply getting the top-1 token correct—which is all that matters for speculative decoding acceptance. This massively diluted the gradient signal and explained the plateau. The user had explicitly rejected sunk cost fallacy ([chunk 0.0]), choosing to abandon the v4 run at epoch 1.93 of 6 rather than continue training a model with a fundamentally broken architecture.

How Decisions Were Made

The decision to kill v4 and archive its artifacts was not made lightly. It followed a multi-step investigative process that combined empirical evaluation, code comparison, and theoretical analysis.

First, the assistant built a comprehensive evaluation harness on CT129 that loaded the target Qwen3.6-27B model using the fla library for correct linear attention, extracted hidden states from 10 fresh coding prompts, and ran drafter inference using a reimplemented standard attention mechanism. A critical sub-discovery during this process was that CPU-based hidden state extraction (using PyTorch's fallback for linear attention) produced numerically different results from the fla-based extraction used during training—4 of 5 target layers use Qwen3.5's linear attention, and the bf16 numerical differences caused completely garbled drafter output. Switching to GPU extraction with fla fixed this and revealed the true performance gap.

Second, the assistant performed a detailed code comparison against the official speculators repository. Reading the official metrics.py ([msg 9127]) confirmed that the reference implementation uses pure hard CE loss with gamma=4.0, no soft KL, and no streak weighting. Reading the pipeline code ([msg 9124]) confirmed the noise-on-targets bug at line 176, where all_packed = all_packed + torch.randn_like(all_packed) * noise_std applied noise before the last layer extraction at line 699.

Third, the fixes were implemented in a series of surgical edits ([msg 9132] through [msg 9144]): reverting fc to (N-1)*H input (4 layers), passing the verifier last hidden state as a separate clean tensor, applying noise only to the aux (fc input) tensor, switching default loss to hard CE with gamma=7.0, and disabling soft labels and streak weighting by default. The code was committed with a detailed commit message ([msg 9147]) documenting all three bugs.

Only then, with the fixes committed and the code syntax-checked, did the assistant execute the kill-and-archive command in [msg 9149].

Assumptions Made

The message embodies several assumptions. The assistant assumes that the tmux session is named "dflash" and is running on container 200 of the Proxmox host at 10.1.2.6. It assumes that checkpoints reside in /workspace/checkpoints/ and that moving them to /workspace/v4_archive/ is the correct archival strategy. It assumes that killing the session with tmux kill-session -t dflash followed by a 3-second sleep is sufficient to cleanly terminate the training process. It assumes that the v4 checkpoints are not worth continuing from—a critical assumption justified by the architectural nature of the bugs, since fixing the fc layer count and noise application changes the model structure itself, making old checkpoints incompatible.

Mistakes and Incorrect Assumptions

The most significant incorrect assumption in the broader arc was that the "improvements" added to the DFlash training—soft KL loss, streak-aware dynamic weighting, higher gamma—were actually beneficial. These had been implemented in earlier segments (<seg id=48>) as sample efficiency improvements, but the comparison against the official code revealed they were actively harmful. The soft KL loss diluted the gradient by forcing the model to match the full 248K-dim distribution. The streak weighting and high gamma further distorted the loss landscape.

Another incorrect assumption was that feeding all 5 target layers to the fc projection was correct. The official architecture uses 4 layers for conditioning and reserves the last (deepest) layer for target logits, because layer 61 (near the last of 64 layers) carries the richest next-token information. By including it in the fc input, the model was essentially seeing the answer before being asked to predict it.

The noise-on-targets bug was a straightforward implementation error—the pipeline applied noise to the combined tensor before the split, when it should have applied noise only to the fc input tensor after splitting.

Input Knowledge Required

To understand this message, one needs knowledge of the DFlash speculative decoding architecture, including the role of the fc projection layer, the verifier head, and the noise schedule. One needs to understand the training pipeline structure: how hidden states are captured from the target model, packed into tensors, and fed to the drafter. One needs familiarity with the infrastructure: the Proxmox host at 10.1.2.6, LXC container 200, tmux session management, and the SSH-based remote execution pattern used throughout the session. One also needs the context of the three bugs discovered in the preceding messages—without that context, the command to kill and archive looks like a routine operation rather than a pivotal decision.

Output Knowledge Created

The message creates concrete output knowledge: v4 training is stopped, its checkpoints (steps 2000 and 4000) and training log are archived to /workspace/v4_archive/, and the workspace is clean for v5 deployment. The output also confirms that the v4 run had only reached step ~5,400 (as seen in the preceding message's tmux capture showing step 5401), meaning it was still early in training—barely past the 5,000-step mark where the user had previously observed the plateau. The archive preserves these artifacts for potential future comparison or analysis, a prudent choice given the experimental nature of the work.

The Thinking Process Visible in the Message

While the message itself is just a command and its output, the thinking process is visible in what it doesn't do. It doesn't delete the v4 checkpoints—it archives them. It doesn't simply kill the session and start v5 in the same directory—it creates a separate archive directory. This reflects a disciplined experimental mindset: preserve evidence, enable future comparison, and maintain a clean separation between experimental iterations. The 3-second sleep after killing the tmux session shows awareness of process cleanup timing. The use of mkdir -p (no error if exists) and the redirection of stderr to stdout (2>&1) show robust scripting practices.

The message also reveals the assistant's prioritization: the commit of fixed code ([msg 9147]) happened before the kill command, ensuring that the corrected codebase was safely version-controlled before any destructive operations. This ordering—fix first, commit second, kill third, archive fourth—reflects a careful, methodical approach to what could otherwise be a panicked abandonment of a failing experiment.

Conclusion

Message [msg 9149] is a turning point in the DFlash training saga. It marks the clean break between a training run built on incorrect assumptions and a corrected run built on verified architectural alignment with the official implementation. The three bugs discovered—noise corrupting targets, fc shortcut, and loss function mismatch—were not incremental issues but fundamental design flaws that made the v4 model incapable of reaching the performance of the reference. By choosing to abandon v4 rather than continue training, the user and assistant demonstrated the discipline required for serious ML engineering: the willingness to discard sunk cost and rebuild on a correct foundation. The v5 run that follows, named v5-hardCE-g7-splitfc-cleanverifier, would go on to incorporate all three fixes and represent the first truly correct implementation of the DFlash training pipeline in this codebase.