The Moment of Truth: Restarting Training After a Critical Vocab Mapping Fix

Message Overview

In this brief but pivotal message, the assistant executes a simple command that represents the culmination of a deep debugging session:

Now let me clear the previous output directory and restart training with the fixed vocab remapping.

>

``bash ssh root@10.1.230.174 'rm -rf /data/eagle3/output_finetune_aqmedai && mkdir -p /data/eagle3/output_finetune_aqmedai' 2>&1 ``

On the surface, this is nothing more than a directory cleanup and a preparation for relaunching a training job. But in the context of the broader conversation, this message marks a critical inflection point: the transition from diagnosis to remedy, from confusion to conviction, and from wasted compute to purposeful iteration.

The Debugging Journey That Led Here

To understand why this message matters, we must trace the path that brought the assistant to this moment. The session had been exploring whether the AQ-MedAI K2 EAGLE-3 drafter could be fine-tuned to work with the Kimi-K2.5 model. The initial probe (Phase 0) was promising: the K2 drafter achieved an accept_len of ~1.5 and 52 tok/s when deployed directly with K2.5 in SGLang (<msg id=4965-4966>). This was worse than the from-scratch drafter (accept_len ~2.0, 60 tok/s) but above 1.0, indicating that the hidden state representations shared meaningful structure. The logical next step was fine-tuning.

Phase 1 launched with a carefully chosen learning rate of 5e-5 (lower than the 3e-5 used for from-scratch training) to gently adapt the K2 representations without destroying them ([msg 4971]). But when the training metrics arrived, they were alarming: loss values of ~18-20 (cross-entropy over a 32,000 vocabulary) and accuracy of 2-7% — essentially random performance ([msg 4973]).

The user immediately spotted the contradiction: "Why is the loss/accuracy basically zero if the model was predicting ok in sglang?" ([msg 4975]). This was the right question. If the same drafter achieved accept_len ~1.5 in SGLang inference, it was clearly predicting above chance. The training pipeline must be feeding the model different data than what SGLang provided at inference time.

The Diagnosis: A Vocab Mapping Mismatch

The assistant's investigation revealed the root cause: a critical vocab mapping mismatch between the AQ-MedAI K2 drafter and the K2.5 training data. The training script loaded AQ-MedAI's lm_head weights, but these weights were trained to output logits for AQ-MedAI's specific draft-to-target token mapping. Meanwhile, the training labels were computed using K2.5's own t2d/d2t mapping. When the lm_head predicted "token A" at draft position 500 (according to AQ-MedAI's mapping), the loss function compared it against a label that said position 500 meant something entirely different (according to K2.5's mapping).

A diagnostic script confirmed the severity: only 252 out of 32,000 draft vocab positions matched between the two mappings ([msg 4980]). The first ~50 positions were shared (likely special tokens and byte-level tokens), but after that the orderings diverged completely. This meant that for 31,748 out of 32,000 positions, the model was being trained with completely wrong labels — pure label scrambling that produced random loss.

The Fix: Remapping the lm_head

The assistant devised a fix: build a permutation matrix from AQ-MedAI's d2t mapping to K2.5's d2t mapping, then remap the lm_head rows accordingly. For the 6,250 AQ-MedAI draft tokens that didn't exist in K2.5's vocab at all, the plan was to initialize those rows randomly or from the verifier's lm_head ([msg 4981]).

The training script (04_train.py) was edited to add this remapping logic in the --finetune-from weight loading block ([msg 4983]). The edit replaced the naive weight loading with a sophisticated remapping that aligned the two vocabularies before training began.## Why This Specific Message Matters

The subject message — the rm -rf and mkdir -p command — is the moment where all the debugging, analysis, and code changes crystallize into action. It is the assistant saying: "I have identified the problem, I have fixed the code, and now I am ready to test whether the fix works."

The rm -rf is particularly significant. It destroys the previous failed training run's output directory, including any partial checkpoints, logs, and intermediate results. This is a deliberate act of resetting — acknowledging that the previous run's output was fundamentally corrupted by the vocab mapping bug and cannot be salvaged. Any artifacts from that run would only cause confusion if inspected later. The mkdir -p that follows creates a clean slate.

This pattern — destroy and recreate — is common in ML engineering but carries weight here because of what it represents. The previous run consumed GPU time, generated logs, and produced metrics that initially looked like a dead end. The assistant could have tried to salvage it, to apply the fix mid-training, or to analyze the corrupted outputs further. Instead, it chose to wipe the slate clean and start fresh with the corrected pipeline.

Assumptions Made

Several assumptions underpin this message:

1. The vocab remapping fix is correct. The assistant assumes that permuting the lm_head rows according to the d2t mapping will align the model's predictions with the training labels. This is a reasonable assumption — the lm_head is a linear layer where each row corresponds to a draft vocab position, and permuting rows is equivalent to permuting the output vocabulary. However, there's a subtlety: the lm_head's weights were trained jointly with the rest of the drafter, and the hidden states flowing into the lm_head may have internal structure that assumes a particular vocab ordering. Remapping only the lm_head rows could leave the hidden states misaligned if the drafter's intermediate layers also encode vocab-specific information.
2. The fc layer does not need remapping. The assistant explicitly checked this and concluded that only lm_head is affected by the vocab ordering because fc takes concatenated hidden states as input (21504 dim → 7168 dim) and is independent of the token mapping. This is correct — the fc layer operates on hidden state representations, not on token IDs.
3. The previous training run's output is worthless. By deleting the entire output directory, the assistant assumes that nothing from the corrupted run is worth keeping. This is almost certainly correct — with random loss, the model weights would have been updated in random directions, and any checkpoint would be worse than the original AQ-MedAI weights.
4. The environment is ready for a clean restart. The assistant had already killed GPU processes and verified that GPUs were free ([msg 4985]). The assumption is that no other processes will interfere with the new training run.

The Thinking Process

The message reveals a clear, methodical thinking process. The assistant had just completed the code edit to add vocab remapping ([msg 4983]), verified that the fc layer didn't need remapping ([msg 4984]), confirmed that post_attention_layernorm was loaded correctly in the previous run ([msg 4985]), and freed the GPUs ([msg 4985]). The sequence of actions shows a systematic approach:

1. Identify the root cause (vocab mapping mismatch)
2. Verify the hypothesis (diagnostic script confirming 252/32000 match)
3. Design the fix (permute lm_head rows using d2t mapping)
4. Implement the fix (edit 04_train.py)
5. Check for side effects (verify fc layer, post_attention_layernorm are fine)
6. Prepare the environment (kill GPU processes, verify free memory)
7. Reset state (rm -rf old output, mkdir fresh)
8. Launch the corrected run (implied next step) This is textbook debugging methodology. The assistant didn't jump to conclusions, didn't try random fixes, and didn't ignore the user's insightful question. Instead, it traced the contradiction (good inference vs. bad training) to its source and applied a targeted correction.

Input Knowledge Required

To fully understand this message, the reader needs:

• Understanding of EAGLE-3 architecture: The drafter uses an lm_head to project hidden states to vocab logits, and the t2d/d2t mappings define which target tokens each draft position corresponds to.
• Knowledge of fine-tuning mechanics: When fine-tuning, the model's weights are initialized from a pretrained checkpoint, and the training labels must align with the model's output distribution.
• Familiarity with the session's history: The AQ-MedAI K2 drafter was probed in Phase 0, the fine-tuning attempt produced random loss, and the vocab mismatch was diagnosed as the cause.
• Understanding of the rm -rf / mkdir -p pattern: This is a standard way to reset a directory for a fresh run in Unix environments.

Output Knowledge Created

This message doesn't produce new knowledge directly — it's an action message that prepares for knowledge creation. The output is:

• A clean output directory ready for the corrected training run
• Confirmation that the assistant has completed the fix and is ready to test
• A clear signal that the debugging phase is over and the execution phase has begun The real knowledge will come from the training run that follows — whether the loss drops to reasonable levels, whether accuracy improves, and ultimately whether the fine-tuned drafter outperforms the from-scratch model.

Broader Significance

This message exemplifies a crucial skill in ML engineering: knowing when to abandon a corrupted run and start fresh. The temptation to "fix it live" — to patch the code and resume training from the last checkpoint — is strong, especially when GPU time is expensive. But the assistant correctly recognized that the corrupted run's weights were updated in random directions (due to wrong labels), making any checkpoint worse than useless. The clean restart was the only rational choice.

The message also demonstrates the value of the user's intervention. The user's question — "Why is the loss/accuracy basically zero if the model was predicting ok in sglang?" — was the catalyst that forced the assistant to look beyond surface-level metrics and find the real bug. Without that question, the assistant might have continued training, watching the loss slowly decrease from random to slightly-less-random, and concluded that fine-tuning from K2 weights was ineffective. Instead, the bug was caught early, the fix was applied, and the true test could begin.

In the end, this message is about intellectual honesty: admitting that the previous run was fundamentally broken, wiping it clean, and trying again with the correct understanding. It's a small action that represents a big mindset.