The Pivot Point: Launching Fixed Fine-Tuning After Diagnosing a Vocab Mapping Catastrophe

In the high-stakes world of speculative decoding for large language models, a single percentage point of accuracy can mean the difference between a viable speedup and a net loss. When the assistant in this opencode session launched the command shown in <msg id=4988>, it represented the culmination of an intense debugging spiral—a moment where diagnosis gave way to action, and a critical bug in the fine-tuning pipeline was put to the test.

The message itself is deceptively simple: a single ssh command that launches a distributed training job on an 8-GPU machine. But to understand why this particular invocation matters, we must trace the chain of reasoning that led to it—a chain that exposes one of the most subtle and destructive bugs in transfer learning for language models: the silent misalignment of vocabulary mappings.

The Context: A Fine-Tuning Gamble

The broader project was ambitious: deploy the Kimi-K2.5 model with EAGLE-3 speculative decoding to achieve throughput gains over the baseline. The team had already trained a from-scratch EAGLE-3 drafter on 100K synthetic samples, achieving 74.7% validation accuracy and a respectable 60 tok/s. But they wanted more. The AQ-MedAI K2 EAGLE-3 drafter—a publicly available checkpoint trained on the related Kimi-K2 architecture—offered a tantalizing shortcut. If fine-tuned on K2.5 data, it might converge faster and perform better than the from-scratch model.

Phase 0 had confirmed the premise was sound: when the AQ-MedAI drafter was deployed directly with K2.5 in SGLang, it achieved an accept length of ~1.5 tokens per speculation step, producing 52 tok/s. This was worse than the from-scratch drafter's ~2.0 accept length, but it was above 1.0—meaning the K2 hidden states were genuinely correlated with K2.5's distribution. Fine-tuning should bridge the gap.

Phase 1 began with high confidence. The training script already supported --finetune-from, the GPUs were freed, and a conservative learning rate of 5e-5 was chosen to gently adapt the K2 representations without destroying them. The command was launched, and the team waited.

The Crash: Random Loss and the First Hint of Trouble

When the first training metrics arrived, they were catastrophic. Loss values of ~18-20 (cross-entropy over a 32,000-token vocabulary) and accuracy of 2-7%—essentially random. The same drafter that had achieved accept_len ~1.5 in SGLang was now performing at chance level in the training loop.

The user immediately spotted the contradiction: "Why is the loss/accuracy basically zero if the model was predicting ok in sglang? Are we passing it correct layers/hidden states etc correctly?" This was the right question, and it triggered a systematic investigation.

The Diagnosis: Only 252 Out of 32,000 Positions Match

The assistant hypothesized three possible causes:

  1. Vocab mapping mismatch: The training labels were computed using one vocabulary mapping, but the drafter's lm_head was trained on a different one.
  2. Hidden state format mismatch: The training data might be structured differently from what SGLang feeds the drafter at inference.
  3. Verifier lm_head misalignment: The loss computation might involve a masked lm_head that doesn't align with the drafter's output space. The first hypothesis proved correct—and the magnitude of the mismatch was staggering. By comparing AQ-MedAI's d2t (draft-to-target) mapping with the project's own mapping, the assistant discovered that only 252 out of 32,000 draft-to-target token positions matched. The first ~50 positions were shared (likely special tokens and byte-level tokens), but after that, the orderings diverged completely. The overlap in target tokens was 25,750 out of 32,000—meaning most of the target tokens existed in both vocabularies—but the positional mapping was almost entirely different. This is the subtle killer: in EAGLE-3, the draft model's lm_head is a linear layer with 32,000 output logits, each corresponding to a "draft token" position. The d2t mapping defines which target token each draft position represents. AQ-MedAI had trained their lm_head so that position 500 predicted target token X, but the training pipeline's labels said position 500 should predict target token Y. The result was pure label scrambling—the model was being penalized for correct predictions because the output space was permuted.

The Fix: Remapping the lm_head

The assistant's fix was elegant: instead of loading AQ-MedAI's lm_head weights directly, the training script would build a permutation matrix from AQ-MedAI's d2t mapping to the project's d2t mapping. Each row of the lm_head would be moved to the position corresponding to the same target token in the project's vocabulary ordering. For the 6,250 AQ-MedAI draft tokens that didn't exist in the project's vocab, the corresponding rows would be initialized from the verifier's lm_head or randomly.

This fix was applied to 04_train.py in <msg id=4983>, and the script was copied to the remote machine in <msg id=4986>.

The Message: Launching v2

The message in <msg id=4988> is the launch of this fixed training run. Every parameter tells a story:

What This Message Reveals About the Debugging Process

The trajectory from the initial random-loss observation to this launch command reveals several important aspects of the assistant's problem-solving approach:

Systematic hypothesis testing: When confronted with the contradiction (good inference performance vs. random training loss), the assistant didn't guess—it enumerated three specific, testable hypotheses and began checking them in order of likelihood.

Data-driven diagnosis: The critical insight came from a quantitative comparison of the two vocabulary mappings. Writing a standalone debug script to compute the overlap (252/32000) transformed a vague suspicion into a concrete, measurable fact.

Minimal, targeted fix: The assistant didn't rewrite the training pipeline or change the architecture. It identified exactly one tensor (lm_head.weight) that needed remapping and modified only the loading logic. This is the hallmark of a precise diagnosis.

Acknowledgment of uncertainty: The log file name v2 implicitly acknowledges that the first attempt failed. The assistant doesn't overclaim—it launches the fix and waits for the results to speak.

Assumptions and Potential Pitfalls

Several assumptions underlie this launch:

  1. The lm_head remapping is sufficient: The assistant assumed that only the lm_head layer needed remapping, not the fc (fully connected) layer or other components. This is correct because fc operates on hidden states, not token IDs, so it's independent of vocabulary ordering.
  2. The AQ-MedAI hidden state representations are compatible: The remapping fixes the output space alignment, but the hidden states themselves were trained on K2 data. They might still encode K2-specific patterns that don't transfer well to K2.5.
  3. The learning rate is appropriate: 5e-5 is a common fine-tuning LR, but the optimal value depends on how different the K2 and K2.5 distributions are. If they're very different, the LR might be too low to escape the K2 attractor; if they're very similar, it might be too high and overshoot.
  4. The verifier lm_head is compatible: The training script uses the verifier's masked lm_head for loss computation. The assistant assumed this masking is consistent with the remapped drafter output space—a reasonable assumption, but one that could hide additional mismatches.

The Broader Significance

This message captures a universal pattern in machine learning engineering: the moment when a debugging investigation reaches its conclusion and the fix is deployed. The command itself is mundane—a distributed training launch with standard parameters. But the context transforms it into a narrative turning point.

The vocab mapping bug is a cautionary tale about the hidden assumptions in transfer learning. When we say "fine-tune from a pre-trained checkpoint," we implicitly assume that the input and output spaces are aligned. In language models, the vocabulary is usually fixed, so this assumption holds. But in EAGLE-3, the draft model operates in a derived vocabulary space defined by the d2t mapping—and different training runs can produce different mappings even when the underlying tokenizer is identical.

The lesson is broader than EAGLE-3: any time a model's output space is defined by a data-dependent mapping (vocabulary pruning, label remapping, feature selection), transferring weights requires careful alignment of those mappings. The 252-out-of-32,000 mismatch is an extreme case, but smaller misalignments could silently degrade performance without producing the obvious random-loss signal that triggered this investigation.

Conclusion

The message in <msg id=4988> is a single bash command—eight lines of shell invocation that launch a distributed training job. But it represents the culmination of a precise, data-driven debugging process that identified and fixed a critical vocab mapping mismatch. The assistant moved from confusion (random loss despite good inference) to diagnosis (252/32000 positions match) to action (remapping the lm_head and relaunching training) in a matter of minutes.

Whether this fix succeeds—whether the fine-tuned AQ-MedAI drafter converges to competitive accuracy—is a question for the next round of metrics. But the process itself is a model of systematic debugging in the complex, multi-layered environment of modern ML systems. The launch command is the payoff: a moment of clarity after confusion, and a test of whether the diagnosis was correct.