The Pivot from Planning to Execution: Phase 0 of the EAGLE-3 K2→K2.5 Probe

Introduction

In the long arc of optimizing Kimi-K2.5 inference throughput on an 8× NVIDIA RTX PRO 6000 Blackwell GPU system, there comes a moment when exhaustive planning must yield to empirical reality. Message [msg 4942] captures exactly that transition. After dozens of messages spanning architecture analysis, performance benchmarking, NCCL tuning, training pipeline debugging, and the creation of a comprehensive game plan document, the assistant finally declares: "The next step is Phase 0: Quick Probe." This single message, though brief in its visible content, represents a critical inflection point — the moment when the assistant shifts from planning what to do to doing what was planned.

The Message in Full

The assistant writes:

Good, I have the full game plan. The next step is Phase 0: Quick Probe — try the AQ-MedAI K2 drafter directly with K2.5 base model to measure how similar the hidden state representations are.

This is followed by a todowrite tool invocation that decomposes Phase 0 into structured sub-tasks:

• Phase 0: Quick Probe — try AQ-MedAI K2 drafter directly with K2.5 (status: in_progress)
• Phase 0.1: Kill any existing server processes (status: in_progress)
• Phase 0.2: Prepare AQ-MedAI drafter directory (fix config, verify vocab mappings) (status: pending)
• Phase 0.3: Launch SGLang with K2.5 base + AQ-MedAI drafter (truncated in display) The message is deceptively simple. On its surface, it is a confirmation and a task declaration. But beneath that surface lies a dense web of context, reasoning, and assumptions that make it one of the most consequential messages in the entire conversation.

Why This Message Was Written: The Context and Motivation

To understand why this message exists, one must trace the thread of reasoning that led to it. The conversation up to this point had been a systematic, multi-phase investigation into why EAGLE-3 speculative decoding was hurting performance rather than helping it. The baseline throughput was 82 tok/s, but the assistant's own EAGLE-3 drafter (trained from scratch on 37K samples) achieved only 60 tok/s — a 27% regression. The root cause had been identified: the verify step consumed ~30ms per cycle, accounting for 97% of the total cycle time, and the bottleneck was NCCL all-reduce communication across PCIe-connected GPUs.

The user had then identified a potential shortcut: the AQ-MedAI team had already trained an EAGLE-3 drafter for Kimi-K2 (the predecessor model) on 1.4 million samples, achieving an accept_len of 3.2–3.5. Since Kimi-K2.5 shares the same DeepSeek V3 / MLA architecture as K2 — same hidden_size (7168), same intermediate_size (18432), same attention heads, same layer structure — the hypothesis was that the AQ-MedAI drafter's weights could serve as a strong initialization for K2.5 fine-tuning, potentially reaching high acceptance rates with far fewer training samples than training from scratch.

The assistant had already downloaded the AQ-MedAI drafter ([msg 4929]), verified every weight shape matched exactly ([msg 4930]), and written a comprehensive game plan document (eagle-k2finetune-game-plan.md) laying out a three-phase approach: Phase 0 (quick probe), Phase 1 (fine-tune on existing 37K samples), Phase 2 (scale up training data). The user had then said "Continue if you have next steps" ([msg 4940]), and the assistant responded by reading the game plan ([msg 4941]) and then producing the subject message.

The motivation is clear: the assistant is executing the user's directive to proceed, following the pre-established plan, and beginning with the cheapest, fastest experiment first. The message is the starting gun for Phase 0.

How Decisions Were Made

The decision-making visible in this message is primarily about task decomposition and sequencing. The assistant has already decided what to do (Phase 0) — that decision was made collaboratively with the user in earlier messages and codified in the game plan. What this message contributes is the how of execution.

The assistant breaks Phase 0 into three sub-steps:

1. Kill existing server processes — A necessary precondition. The SGLang server is currently running EAGLE-3 2-step with decode attention mode (as noted in the game plan). Before launching a new configuration, all GPU processes must be terminated and GPU memory freed.
2. Prepare the AQ-MedAI drafter directory — The downloaded model needs configuration adjustments. Specifically, the max_position_embeddings field must be changed from 131072 to 262144 to match K2.5's requirements, and the vocab mappings (t2d/d2t) must be verified to be compatible.
3. Launch SGLang with the new configuration — Start the server with K2.5 as the base model and AQ-MedAI's drafter as the speculative draft model, then benchmark the resulting throughput and acceptance rate. The use of the todowrite tool is itself a decision about how to manage complex multi-step workflows. Rather than keeping the task list in working memory or relying on a mental checklist, the assistant externalizes it into a structured todo list that can be updated as steps complete. This is particularly valuable in a session where the assistant may need to handle tool results from multiple parallel invocations and track progress across many rounds.

Assumptions Embedded in This Message

Every action plan rests on assumptions, and this message is no exception. Several critical assumptions are baked into the decision to proceed with Phase 0:

1. The K2 and K2.5 hidden state representations are similar enough. This is the central hypothesis being tested. The AQ-MedAI drafter was trained on hidden states extracted from Kimi-K2, not K2.5. While the architectures are identical, the actual hidden state values at layers [2, 30, 58] could differ significantly if K2.5's weight distribution shifted during its own training. The probe will measure this directly via the acceptance rate, but the assumption that the probe is worth doing is that there's a non-trivial chance of similarity.

2. The vocab mappings are compatible. The AQ-MedAI safetensors file includes t2d and d2t tensors (target-to-draft and draft-to-target token mappings). These mappings were built for K2's vocabulary. If K2.5 changed its vocabulary (added tokens, reordered them, etc.), the mappings would be incorrect and the drafter would produce garbage. The assistant plans to verify this in Phase 0.2.

3. The existing infrastructure is ready. The assistant assumes that the SGLang installation, NCCL tuning (persisted via sitecustomize.py), and GPU environment are all in a working state. The game plan notes that NCCL tuning is critical — without it, baseline drops from 82 to 63 tok/s. The assistant assumes this tuning is still active.

4. Phase 0 will take ~30 minutes. This estimate from the game plan assumes a straightforward launch and benchmark. If configuration issues arise (wrong config fields, missing vocab mappings, SGLang compatibility problems), the probe could take much longer.

5. The drafter can be used "as-is" without fine-tuning. The probe is explicitly testing the untrained AQ-MedAI drafter on K2.5. If the acceptance rate is high (>2.5), it would suggest the models are so similar that fine-tuning might not even be necessary. If it's low (~1.0), it confirms that fine-tuning is essential. The assumption is that even a low acceptance rate provides useful information.

Mistakes and Incorrect Assumptions

With the benefit of hindsight from the chunk summary, we know that several of these assumptions proved problematic. The Phase 0 probe achieved an accept_len of approximately 1.5 and 52 tok/s — confirming architectural compatibility but revealing misaligned hidden state representations. The acceptance rate of 1.5 was well below the break-even threshold of 2.46, meaning the drafter was still hurting performance.

More significantly, when the assistant proceeded to Phase 1 (fine-tuning the K2 drafter on K2.5 data), the initial loss was random (~18–20), which was diagnosed as a critical vocab mapping mismatch: only 252 out of 32,000 draft-to-target token positions matched between the AQ-MedAI and K2.5 mappings. This was a deeper problem than anticipated — the vocab mappings weren't just slightly different, they were almost entirely incompatible.

After fixing the vocab mapping, the loss dropped to ~9 and accuracy improved to ~24%, but the fine-tuning plateaued at ~38% accuracy — converging far slower than the from-scratch model (which reached 75% by epoch 5). The conclusion was that the K2 weights were a poor initialization for K2.5, and the approach was abandoned.

The core incorrect assumption was that architectural identity implies representational similarity. Just because two models share the same layer dimensions and structure doesn't mean their internal hidden states at specific layers occupy the same representational space. The K2 drafter had learned to predict tokens based on K2's hidden state patterns, and those patterns were sufficiently different from K2.5's that the learned weights provided little benefit.

Input Knowledge Required

To fully understand this message, one needs substantial context:

• EAGLE-3 speculative decoding architecture: How draft models predict multiple future tokens in parallel, how the verify step checks them against the target model, and how acceptance rate relates to throughput.
• The performance baseline: 82 tok/s without speculation, 60 tok/s with the existing drafter, and the 30ms verify cycle bottleneck.
• The AQ-MedAI K2 drafter: Its provenance (trained on 1.4M K2 samples), its architecture (identical to the K2.5 drafter), and its known acceptance rate (3.2–3.5 on K2).
• The game plan document: Its three-phase structure, the rationale for starting with Phase 0, and the detailed infrastructure information it contains.
• The infrastructure topology: 8× PCIe-connected Blackwell GPUs, the Proxmox host/LXC container setup, NCCL tuning persistence, and the SGLang development installation.
• The training pipeline: How hidden states are extracted, how vocab mappings are built, and how the drafter is trained. Without this knowledge, the message reads as a simple "let's try this model" — but with it, one recognizes the careful, data-driven reasoning behind even this preliminary probe.

Output Knowledge Created

This message creates several forms of output knowledge:

1. A structured execution plan — The todowrite items decompose Phase 0 into actionable sub-steps with clear status tracking. This is both a record of intent and a tool for progress monitoring.
2. Confirmation of the plan's readiness — By stating "Good, I have the full game plan" and proceeding to execution, the assistant implicitly validates that the planning phase is complete and all necessary information has been gathered.
3. A timestamp for the beginning of Phase 0 — In a long conversation spanning many messages, this message serves as a clear boundary marker between "planning" and "execution" phases.
4. The foundation for subsequent analysis — The results of Phase 0 (accept_len ~1.5, 52 tok/s) will be compared against the baseline and against the existing drafter's performance, feeding into the decision of whether to proceed to Phase 1.

The Thinking Process

The thinking visible in this message is structured and methodical. The assistant:

1. Confirms understanding by stating "Good, I have the full game plan" — acknowledging that the document has been read and internalized.
2. Identifies the next logical step by referencing Phase 0 from the game plan — following the pre-established priority order.
3. Decomposes the phase into sub-steps — breaking "try the AQ-MedAI K2 drafter directly with K2.5" into kill-processes, prepare-directory, and launch-server. This decomposition reveals an understanding of the operational dependencies: you can't launch a new server while an old one is running, and you can't launch with a misconfigured model.
4. Uses the todowrite tool to externalize the task list — a metacognitive strategy that offloads working memory and enables systematic progress tracking across multiple rounds of tool execution.
5. Sets statuses appropriately — marking the overall phase and the first sub-step as "in_progress" while leaving the dependent steps as "pending." This reflects an understanding of the sequential nature of the work. The thinking is not just about what to do, but about how to manage the complexity of doing it. In a session where the assistant may issue multiple parallel tool calls and wait for results, having an externalized todo list prevents getting lost in the weeds.

Conclusion

Message [msg 4942] is a hinge point in the conversation. It closes the chapter on planning and opens the chapter on execution. While the Phase 0 probe would ultimately reveal that the K2→K2.5 transfer was not as straightforward as hoped — leading to the abandonment of the fine-tuning approach and a pivot to system-level verify optimization — this message represents the necessary first step of empirical validation. The assistant could have spent more time theorizing about whether the transfer would work, but instead chose to run the experiment. That decision — to test rather than speculate — is the hallmark of the scientific approach that characterizes this entire session.

The message also demonstrates a key principle of effective AI-assisted development: decompose complex tasks into verifiable sub-steps, externalize your task tracking, and always start with the cheapest experiment that can falsify your hypothesis. Phase 0 was that experiment, and this message was its declaration.