The Moment of Truth: Reading the Metrics from a Restarted EAGLE-3 Training Run

Message Overview

In message 3472 of this opencode session, the assistant executes a single bash command on a remote machine to inspect the training log of an EAGLE-3 speculative decoding drafter being trained for the Kimi-K2.5 large language model. The command and its output are deceptively simple:

ssh root@10.1.230.174 "grep 'speculators.metrics' /data/eagle3/synth_10k_sglang/train_v2.log | wc -l; echo '---'; grep 'speculators.metrics.*val' /data/eagle3/synth_10k_sglang/train_v2.log | head -3"

The response reveals two critical pieces of information: 32,265 metric log lines have been recorded, and three validation metric entries are visible, starting with epoch 1's validation loss of 6.27 and a step 0 accuracy of approximately 74%.

Context: Why This Message Was Written

This message sits at a pivotal moment in a long and arduous journey to build a working EAGLE-3 speculative decoding system. The broader narrative spans multiple segments of work: tuning SGLang performance to 90 tok/s, developing a server-side hidden state extraction patch, extracting 10K training samples, and then training a new EAGLE-3 drafter from scratch. But the immediate context is the training run itself.

The previous training attempt had produced a drafter with a disastrous ~25% acceptance rate — barely better than random guessing. The root cause was traced to a logging bug: the speculators library's metric_logger had no Python logging handler configured, so all per-step loss and accuracy metrics were silently discarded. The training was happening (the GPU was at 98% utilization), but the team had no visibility into whether the model was actually learning. The assistant had killed the first training run mid-epoch, added logging.basicConfig() to the training script, enabled checkpoint resumption, and restarted from epoch 1.

Now, the user has asked a direct question: "create loss/acc charts so far and save here in ./train-progress/.svg/.png." Before the assistant can generate charts, it needs to know what data is available. This message is the reconnaissance step — checking whether the logging fix worked, how many metric points exist, and what the validation numbers look like.

The Thinking Process Visible in the Command

The command reveals a careful, layered diagnostic strategy. The assistant uses two grep commands separated by a delimiter. The first (wc -l) counts total metric lines — a quick sanity check to confirm the logging fix is producing output. The second extracts validation metrics specifically, because those are the epoch-level aggregates that give the clearest picture of model quality.

The choice to look at validation metrics (rather than training metrics) is significant. Training metrics fluctuate wildly from step to step as the model sees different samples. Validation metrics, computed over the entire held-out set at the end of each epoch, provide a stable, comparable signal. The head -3 limit is also deliberate — the assistant knows the training has only completed through epoch 1 (it was restarted from epoch 1 after the fix), so there should be at most a few validation entries.

The output confirms the strategy worked. With 32,265 metric lines, the logging is clearly producing data at every training step (9000 steps per epoch × ~3.6 epochs = ~32,400, matching closely). The validation metrics show concrete numbers: a step 0 accuracy of 74%, step 1 conditional accuracy of 64%, and step 2 conditional accuracy of 56%. These are dramatically better than the previous drafter's 25% acceptance rate.

Assumptions and Their Validity

Several assumptions underpin this message. The assistant assumes that the speculators.metrics string is a reliable marker for metric log lines — this is correct, as the metric_logger uses that logger name. It assumes that validation metrics appear at the end of each epoch, which is confirmed by the Trainer code structure. It assumes that head -3 will capture all available validation entries (epoch 1 complete, epoch 2 in progress), which proves accurate.

One implicit assumption is that the training is proceeding normally. The assistant doesn't verify GPU utilization, process health, or disk space in this message — it trusts that the earlier restart was successful and the process is still running. This is a reasonable risk: the previous check (message 3467) confirmed the process started and was resuming from epoch 1.

Input Knowledge Required

To fully understand this message, a reader needs to know:

  1. The EAGLE-3 architecture: EAGLE-3 is a speculative decoding framework that trains a lightweight "drafter" model to predict multiple future tokens in parallel, conditioned on the base model's hidden states. The metrics loss_0, full_acc_0, cond_acc_0 refer to the first predicted token; loss_1, full_acc_1, cond_acc_1 to the second token; and so on.
  2. The speculators library: This is the training framework being used. Its Trainer class uses a metric_logger from Python's logging module to record per-step metrics. Without a configured handler, these metrics are silently dropped — the bug that necessitated the restart.
  3. The training pipeline: The model is trained on 10,000 samples of hidden states extracted from the Kimi-K2.5 model running on SGLang. Each sample contains the hidden states at layers [3, 31, 59] for a sequence of up to 2048 tokens. The training uses 3 "TTT steps" (test-time training steps), meaning the drafter predicts 3 future tokens at each position.
  4. The validation split: 10% of the 10,000 files (1,000) are held out for validation. The validation metrics are computed over all 1,000 validation batches at the end of each epoch.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

Mistakes and Incorrect Assumptions

One subtle issue: the assistant counts 32,265 metric lines but doesn't verify that these are all training metrics versus a mix of training and validation metrics. The validation metrics are logged at the end of each epoch, so the count includes both. For charting purposes, the assistant will need to separate training step metrics (logged every step) from validation epoch metrics (logged once per epoch). The grep pattern speculators.metrics captures both, so the assistant will need additional filtering to produce clean training curves.

Additionally, the assistant doesn't check whether the training process is still alive or whether the GPU is still at 98% utilization. If the process had crashed between the restart and this check, the 32,265 lines would be from a dead process. The validation metrics at epoch 1 suggest the process completed at least one epoch, but the assistant doesn't confirm it's still running for epoch 2.

The Broader Significance

This message represents the transition from debugging infrastructure to evaluating results. For the preceding hours, the team had been fighting toolchain issues: SGLang deadlocks, NCCL tuning, hidden state extraction bugs, and logging configuration problems. The training was running but invisible — a black box consuming GPU cycles with no feedback. Message 3472 is the moment the black box opens.

The 74% step 0 accuracy is particularly significant. In speculative decoding, the drafter's first-token prediction accuracy directly determines the acceptance rate — the fraction of draft tokens that the base model accepts. A 74% accuracy means roughly 3 out of 4 first draft tokens will be accepted, which translates to a meaningful speedup over standard autoregressive decoding. This validates the entire approach: training from scratch on SGLang-extracted hidden states produces a dramatically better drafter than the previous attempt.

The assistant's next action — generating charts — will make this data visible and shareable, turning raw log lines into actionable insight. But message 3472 is the foundation: the reconnaissance that confirms the data exists, the metrics are meaningful, and the training is succeeding.