The Moment of Truth: Visualizing EAGLE-3 Training Progress

A Single Image Read That Captures Hours of Work

[assistant] [read] /tmp/eagle3_training_progress.png
Image read successfully

At first glance, message 4303 in this opencode session appears trivial: a single tool call reading a PNG image file, followed by a success confirmation. But this message is anything but trivial. It represents the culmination of a multi-day journey through data generation, hidden state extraction, debugging Triton shared-memory out-of-memory errors, fixing packing logic in data loaders, and orchestrating a distributed training run across four NVIDIA RTX PRO 6000 Blackwell GPUs. The image being read — /tmp/eagle3_training_progress.png — is the first visual evidence of whether the entire EAGLE-3 speculative decoding drafter training pipeline has succeeded or failed.

The Context: Why This Message Exists

To understand why message 4303 was written, we must trace the chain of events that led to it. The conversation immediately preceding this message shows a user who has been monitoring a long-running training job with intense interest. At [msg 4295], the user simply asks "check how train is going." The assistant responds by SSHing into the remote machine and parsing the training logs ([msg 4296]), revealing that the model has completed 10,604 metric log lines — approximately 2,656 training steps, or 60% through epoch 1 of 5.

The training metrics are remarkably good. The assistant reports at [msg 4298] that the drafter's step 0 (next-token prediction) accuracy has reached 74–81%, with conditional accuracy staying above 50% even at depth 4. This is dramatically better than the previous 10K-sample drafter, which achieved only ~2.1 tokens of acceptance length. The assistant estimates the new model will achieve ~3.2–3.5 tokens of acceptance length — a 50% improvement that could make speculative decoding net-positive for the first time in this project.

The user then asks at [msg 4299]: "graph learning progress so far." This is a natural request: after hours of training and seeing raw numbers, the user wants a visual representation of the learning curves. The assistant responds by:

1. Extracting the raw data ([msg 4300]): SSHing into the training machine and grepping all 16,868 metric log lines into a local file.
2. Writing a plotting script ([msg 4301]): Creating a Python script (plot_training.py) that parses the JSON-formatted metric lines and generates a multi-panel chart showing loss, full accuracy, and conditional accuracy across TTT steps 0–4.
3. Running the script ([msg 4302]): Executing the plotter, which parses 4,217 steps (deduplicating across 4 GPU ranks) and saves the PNG to /tmp/eagle3_training_progress.png.
4. Reading the image ([msg 4303]): Using the read tool to load the generated PNG and present it to the user. Message 4303 is the final link in this chain — the delivery mechanism for the visualization the user requested.

The Reasoning and Decision-Making Behind the Message

The assistant's decision to use the read tool on the PNG file reflects several layers of reasoning about the opencode environment and user expectations.

First, the assistant assumes that the read tool can handle image files and will render them in a way visible to the user. In many coding assistant interfaces, reading an image file displays it inline, making it immediately viewable. This is a reasonable assumption given the tool's design for reading various file types.

Second, the assistant chose to generate a static PNG visualization rather than attempting to stream live metrics or create an interactive dashboard. This decision reflects a pragmatic trade-off: the training is running on a remote machine with no web server, the user wants a quick visual check, and a static plot is the fastest path to insight. The plotting script itself (written at [msg 4301]) is a focused 60-line Python file using matplotlib — no dependencies beyond the standard data science stack.

Third, the assistant made a deliberate choice about what to visualize. The plot shows five TTT (tree-attention) steps — the multi-token prediction depth used in EAGLE-3 training. By plotting loss, full accuracy, and conditional accuracy for each step, the chart gives the user immediate insight into whether the drafter is learning to predict multiple tokens ahead, which is the core requirement for effective speculative decoding. This is not a generic "loss goes down" chart; it's a targeted diagnostic for the specific architecture being trained.

Assumptions Made

Several assumptions underpin this message and the actions leading to it:

1. The training is still running and producing valid metrics. The assistant assumes the 16,868 lines of metrics represent genuine training progress, not corrupted logs or repeated entries from restarts. This assumption is validated by the consistent improvement visible in the numbers.
2. The plotting script will work on the local machine. The assistant runs the plotter locally (not on the remote training machine), which assumes that the local environment has matplotlib installed and that the metric file format is consistent. The LSP errors shown in the script write output (unresolved imports for torch, transformers, etc.) are irrelevant because those imports are only used in the training script, not the plotter.
3. The user can interpret the chart. The assistant provides no explanation of what the chart shows in the message itself — it simply reads and presents the image. This assumes the user has enough context (from the previous discussion of TTT steps, accuracy metrics, and acceptance length calculations) to understand what they're looking at.
4. The read tool is the correct way to present visual results. In some environments, images might need to be served via a URL or embedded differently. The assistant assumes the opencode interface supports inline image display.

Input Knowledge Required

To fully understand message 4303, a reader needs knowledge of:

• The EAGLE-3 architecture: EAGLE-3 is a speculative decoding framework that trains a lightweight "drafter" model to predict multiple future tokens in parallel using a tree-attention mechanism. The TTT (tree-attention training) steps parameter controls how many future tokens the model learns to predict simultaneously.
• The training pipeline: The 100K-sample dataset was generated through a complex pipeline involving OpenRouter API calls, hidden state extraction via SGLang server patches, and data shuffling. The training itself uses torchrun with 4 GPUs, packing multiple samples into each sequence via a custom collate function.
• The hardware context: Training runs on 4 RTX PRO 6000 Blackwell GPUs (SM120 architecture) connected via PCIe, with 98 GB VRAM each. The Triton shared-memory OOM bug at large sequence lengths (16384+) is specific to this GPU architecture's 101,376 byte shared memory limit per block.
• The previous drafter's performance: The 10K-sample drafter achieved only ~2.1 tokens acceptance length, making speculative decoding slower than the 90 tok/s baseline. The new 100K-sample drafter with TTT=5 is expected to reach ~3.2–3.5 tokens.

Output Knowledge Created

Message 4303 itself creates relatively little new knowledge — it's a delivery action. But the image it reads creates substantial output knowledge:

• Visual validation of training convergence: The user can see whether loss is decreasing smoothly, whether accuracy is improving, and whether there are signs of overfitting or divergence.
• Comparative insight across TTT depths: The multi-panel chart shows how the model performs at each prediction depth, revealing whether deeper predictions are learning or remaining near-random.
• Decision support for next steps: Based on the chart, the user can decide whether to let training continue to all 5 epochs, whether to adjust hyperparameters, or whether to stop early and deploy the current checkpoint for benchmarking.

The Thinking Process Visible in Preceding Messages

The assistant's reasoning is most visible in the messages leading up to 4303. At [msg 4298], the assistant performs a detailed analysis of the raw metrics, computing the expected acceptance length as a chain product of conditional probabilities:

1 + 0.77 + 0.77×0.65 + 0.77×0.65×0.60 + 0.77×0.65×0.60×0.57 + 0.77×0.65×0.60×0.57×0.53 ≈ ~3.2-3.5 tokens

This calculation reveals sophisticated reasoning about how speculative decoding works in practice: the acceptance rate at each depth depends on the conditional accuracy at that depth given that all previous tokens were accepted. The assistant is not just reporting numbers — it's translating raw accuracy metrics into a meaningful performance prediction.

The assistant also shows awareness of the training's early stage, noting "this is only 60% through epoch 1 of 5" and estimating ~9.5 more hours remaining. This contextualizes the visualization: the chart shows a model that is already performing well but still has significant room for improvement.

Broader Significance

Message 4303 represents a transition point in the project. Up to this point, the work has been about building infrastructure: generating data, fixing bugs, getting training to run. The image read signals a shift toward evaluation and deployment. The user now has visual evidence that the 100K-sample drafter is converging well, and the conversation can move toward benchmarking, acceptance rate measurement, and ultimately deploying the drafter with SGLang speculation to beat the 90 tok/s baseline.

The message also illustrates a key dynamic of AI-assisted development: the assistant handles the entire pipeline from data extraction to visualization generation to presentation, while the user provides high-level direction ("graph learning progress so far"). The assistant's ability to parse log files, write plotting code, execute it, and deliver the result in a single coherent sequence demonstrates the power of tool-augmented language models for complex engineering workflows.

In the end, message 4303 is a quiet victory lap — a simple file read that says, without fanfare: the training is working, the model is learning, and here is the proof.