The Clean Restart: Recovering from an OOM and Launching a Corrected Training Run

Message 9084 — A single bash command that represents the culmination of a deep debugging cycle

In the course of training a DFlash drafter model for speculative decoding with the Qwen3.6-27B architecture, the assistant sent a remarkably brief message that belies the depth of reasoning that preceded it. Message [msg 9084] reads in its entirety:

Clean up the failed run's checkpoint dir and restart: ``bash ssh -o ConnectTimeout=10 root@10.1.2.6 'pct exec 200 -- bash -c " rm -rf /workspace/checkpoints/* tmux new-session -d -s dflash \"PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True /root/start_training.sh\" echo started "' 2>&1 started ``

On its surface, this is a mundane operational command: delete old checkpoints and relaunch a training job inside an LXC container on a remote Proxmox host. But to understand why this message was written, one must trace the chain of events that led to this precise moment — a chain that spans architectural debugging, memory budget analysis, and a deliberate decision to abandon sunk costs in favor of a clean slate.

The Context: Why a Clean Restart Was Necessary

The message is the final step in a multi-hour debugging session that began when the assistant built a comprehensive evaluation harness to compare the DFlash drafter's performance against a reference model from the z-lab repository. That evaluation revealed a staggering 4× gap: the assistant's model achieved a DDTree-8 acceptance rate (τ) of approximately 3.0 on fresh coding prompts, while the z-lab reference model achieved τ≈12.4. This gap was traced to three critical bugs in the training setup, documented across [msg 9060] through [msg 9083]:

1. Architectural mismatch: The fc projection layer used only 4 of the 5 available target layers (20480→5120 dimensions), reserving layer 61 exclusively for verifier loss computation. The z-lab model concatenated all 5 layers (25600→5120), and layer 61 — being the deepest layer in a 64-layer model — carried the richest next-token information. The assistant's model never saw it at inference time.
2. Noise corrupting target logits: The noise schedule was applied to the combined 5-layer hidden state tensor before extracting the last layer for target logit computation, directly corrupting the training signal.
3. Loss function mismatch: The official DFlash paper uses pure hard cross-entropy loss with gamma=4.0, while the assistant's implementation used a composite loss (70% soft KL divergence at temperature 2.0 + 30% CE + streak-aware weighting + gamma=10), which diluted gradients by forcing the model to match the full 248K-dim distribution instead of just getting the top-1 token correct. These discoveries led to the "v4" revision, committed in [msg 9065] with three changes: expanding fc to use all 5 layers, reducing noise (0.1→0.01 start, 0.01→0.001 end), and increasing max_anchors from 512 to 1024. The rationale for the anchor increase was that the DFlash paper uses 512 anchors for a max sequence length of 3072 tokens, while the assistant's pipeline uses max_seq_len=8192 — nearly 2.7× longer — so scaling anchors proportionally seemed reasonable.

The OOM: Theory Meets GPU Memory Constraints

The v4 run was launched in [msg 9077]. Within minutes, it crashed with an out-of-memory (OOM) error on GPU 7, the drafter GPU. Message [msg 9080] contains the assistant's detailed reasoning as it diagnosed the failure:

OOM on GPU 7 (drafter) at the KL divergence computation! The issue is that with 1024 anchors × 16 block_size = 16384 block tokens, the KL divergence computation creates tensors of shape [1, 16384, 248320] which is 16384 × 248320 × 2 bytes = ~8 GB. Plus the student log probs and teacher probs (another 8 GB each) = ~24 GB total.

The assistant then walked through a meticulous memory budget analysis: the drafter model itself (3.5 GB), hidden states (~2.5 GB), fc output (0.5 GB), attention KV cache across 5 layers (~0.26 GB per layer), logits tensor (8.1 GB), targets (8.1 GB), and KL divergence temporary buffers (tripling the logits to ~24 GB). The total came to approximately 48 GB for the forward pass alone, plus another ~8 GB for gradients and activation memory for backpropagation, pushing the total close to 95 GB — the full capacity of an RTX PRO 6000 Blackwell GPU.

The OOM error message showed that the system tried to allocate 7.58 GB when only 6.59 GB was available — a gap of roughly 1 GB. The assistant considered several mitigations: reducing max_anchors to 768 (saving ~8 GB), implementing chunked KL computation (processing in batches of 4096 tokens), or reverting to 512 anchors. After weighing the options, the assistant chose the safest path: revert to 512 anchors, which matched the paper's proven configuration and had worked in the v3 run without OOM issues.

The Decision to Clean Up and Restart

This brings us to message [msg 9084]. The assistant had already updated the start_training.sh script with --max-anchors 512 in [msg 9081], reverted the default value in the pipeline script in [msg 9082], and synced the corrected script to the container in [msg 9083]. Now it faced a choice: should it resume from the partial v4 checkpoint (which had completed step 0 and part of step 1 before crashing), or start completely fresh?

The decision to rm -rf /workspace/checkpoints/* — deleting all checkpoints — is a deliberate architectural choice. The v4 run had barely begun (only a single step had partially completed before the drafter thread OOM'd), so there was no meaningful training progress to preserve. More importantly, the configuration had changed: max_anchors was reduced from 1024 to 512, which affects the data layout, the number of block tokens per batch, and potentially the gradient statistics. Resuming from a checkpoint created with a different anchor count could introduce subtle inconsistencies. A clean start eliminates any risk of checkpoint-induced corruption.

The assistant also chose to keep the PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True environment variable, which allows PyTorch's CUDA allocator to expand memory segments on demand rather than pre-allocating the full memory pool. This is a prudent choice given that the OOM was caused by peak memory usage during KL divergence computation, and expandable segments can help smooth out allocation spikes.

Assumptions and Knowledge Required

To understand this message, one needs significant background knowledge:

• The DFlash architecture: Understanding that the drafter is a small transformer (1.73B parameters) trained to predict draft tokens conditioned on hidden states from a frozen target model (Qwen3.6-27B). The fc projection maps target hidden states to the drafter's embedding dimension, and the number of target layers used directly affects both model quality and memory consumption.
• GPU memory budgeting: Knowing how to estimate memory usage for transformer training — parameter storage (2 bytes per param in bf16), activation memory (which scales with batch size and sequence length), and the specific memory footprint of KL divergence loss (which materializes the full vocabulary distribution).
• The LXC/Proxmox infrastructure: The pct exec 200 command executes inside LXC container 200 on the Proxmox host at 10.1.2.6. The tmux new-session -d -s dflash creates a detached tmux session so the training continues running after the SSH connection closes.
• The training pipeline's data flow: Understanding that max_anchors determines how many anchor positions are sampled per batch, which multiplies with block_size (16) to determine the total number of block tokens processed by the drafter.

What This Message Creates

The output of this message is a running training job — the v4 run with the corrected 512-anchor configuration. The started response confirms that the tmux session was created and the training script began executing. This run would go on to be monitored in subsequent messages, with the assistant checking its progress and verifying that the OOM issue was resolved.

But the message also creates something less tangible: a clean demarcation point. By deleting the old checkpoints and restarting, the assistant establishes a clear boundary between the failed v4 attempt (with 1024 anchors) and the corrected v4 run (with 512 anchors). This is important for reproducibility — if the training needs to be resumed later, there is no ambiguity about which configuration was used.

The Thinking Process Visible in the Message

While message [msg 9084] itself contains no explicit reasoning (it is purely operational), it embodies the reasoning from the preceding message [msg 9080]. The assistant's thinking there shows a clear pattern:

1. Observation: The OOM occurs at the KL divergence computation, not during model forward or backward pass.
2. Quantification: Breaking down the memory footprint of each component, identifying the KL computation as the dominant term (~24 GB for temporary buffers).
3. Hypothesis generation: Considering multiple fixes — reducing anchors, chunking the KL computation, or both.
4. Trade-off analysis: Weighing the benefits of 1024 anchors (more training signal per forward pass) against the OOM risk and the complexity of implementing chunked KL computation.
5. Decision: Choosing the simplest, safest fix (512 anchors) that matches the paper's proven configuration and avoids introducing new code complexity. This pattern — observe, quantify, hypothesize, trade off, decide — is characteristic of effective ML engineering debugging. The assistant resisted the temptation to implement a more complex fix (chunked KL computation) that would have preserved the 1024 anchors, recognizing that the marginal benefit of more anchors was unlikely to justify the implementation risk and ongoing OOM fragility.

Conclusion

Message [msg 9084] is a study in how the most impactful actions are often the simplest ones. After hours of deep architectural debugging — tracing performance gaps, comparing against reference implementations, fixing the fc layer count, correcting noise and loss functions — the final step to getting a working training run was a single rm -rf and a relaunch. The message demonstrates that effective ML engineering is not just about identifying complex bugs; it is also about knowing when to clean up, reset, and try again with a corrected configuration. The assistant's willingness to abandon the 1024-anchor experiment rather than engineer around the OOM reflects a pragmatic understanding that training throughput and stability matter more than squeezing extra signal from each batch.