The Empty Reasoning Problem: A Pivot Point in EAGLE-3 Training Data Generation

Message Overview

In message 2918 of a long-running opencode session dedicated to deploying and optimizing large language models on 8× NVIDIA Blackwell GPUs, the user delivers a concise but consequential observation. The message reads:

Let's cap at 10k samples. Also looks like we're not capturing reasoning correctly: { "sample_id": 8526, "messages": [...], "reasoning": "", "content": " Here's the step-by-step calculation...", ... } { "sample_id": 2983, "messages": [...], "reasoning": "", "content": " Grandma Olga has 33 grandchildren in total...", ... } -- btw when reassembling we should append with correct Think tokens

This message appears at a critical juncture in the session. The team has spent hours building an EAGLE-3 speculative decoding training pipeline for the Kimi-K2.5 model — a 1-trillion-parameter Mixture-of-Experts model deployed on 8× RTX PRO 6000 Blackwell GPUs. The pipeline's first stage involves generating synthetic training data by feeding questions from the mlabonne/open-perfectblend dataset through the vLLM inference server and capturing the model's responses. The captured responses are then used to train a lightweight EAGLE-3 draft model that can accelerate inference through speculative decoding.

The message identifies two distinct problems in this data generation process, and it does so with remarkable precision, backed by concrete evidence from the captured JSON output.

Context: The State of the Pipeline

To understand why this message matters, we need to trace the session's trajectory. The team had been working on speculative decoding for Kimi-K2.5 since Segment 19 of the conversation. After ruling out n-gram speculation as too slow (Segment 20), they built a complete EAGLE-3 training pipeline from scratch, patching the speculators library for API compatibility with vLLM 0.16 (Segment 21), and successfully ran training end-to-end on 1000 samples (Segment 22, Chunk 0).

The user then redirected the approach. Instead of training on hidden states extracted from the model's own outputs, the user wanted to generate higher-quality training data by capturing Kimi-K2.5's actual reasoning outputs — the model's internal chain-of-thought before producing a final answer. This is a crucial insight: for speculative decoding to work well, the draft model needs to predict not just what the model says, but how it thinks. The reasoning trace is where the model's computational "effort" lives, and an EAGLE-3 draft model trained on these traces can learn to anticipate the verifier model's next tokens more accurately.

The assistant wrote 01b_generate_synthetic.py to implement this approach. The script feeds each question from open-perfectblend independently through the vLLM inference server at high concurrency (initially C=200), capturing both the reasoning field and content from the model's responses. During initial testing, two issues emerged: requests timing out due to the default 60s client timeout, and the reasoning field not being captured because the script was checking reasoning_content instead of the correct reasoning attribute. The assistant fixed both issues and restarted the run with C=128 concurrency and an 1800s timeout.

By the time of message 2918, the new run had been running for some time. The assistant had reported 388 samples saved to disk ([msg 2920]). But the user, monitoring the output, spotted a deeper problem.

The Core Discovery: Reasoning Is Empty

The user presents two JSON records from the captured data. Both show the same troubling pattern:

{
  "sample_id": 8526,
  "reasoning": "",
  "content": " Here's the step-by-step calculation...",
  ...
}
{
  "sample_id": 2983,
  "reasoning": "",
  "content": " Grandma Olga has **33** grandchildren in total...",
  ...
}

The reasoning field is an empty string. The model is producing direct answers — step-by-step solutions, yes, but without any internal reasoning trace. This is a fundamental problem for the training data pipeline.

Why does this matter? Kimi-K2.5 is a reasoning model. It is designed to produce an internal chain-of-thought before answering. This reasoning is typically wrapped in special tokens — <think> (token 163606) and <response> (token 163607) — that delimit the reasoning phase from the answer phase. The reasoning trace is where the model explores possibilities, checks its logic, and arrives at conclusions. For EAGLE-3 training, this reasoning trace is arguably more valuable than the final answer, because it contains the model's internal computation — the very thing the draft model needs to learn to predict.

The empty reasoning field suggests one of two possibilities: either the model is not being prompted to reason (it's producing answers directly without a reasoning phase), or the reasoning content is being captured but stored in the wrong field. The user's evidence points to the first possibility: the completions are only 280 and 274 tokens respectively for math problems that would normally trigger extensive reasoning. A model that's actually reasoning would produce a much longer completion, with the bulk of tokens in the reasoning phase.

The Second Concern: Correct Think Tokens for Reassembly

The user adds a critical note: "btw when reassembling we should append with correct Think tokens." This refers to the next stage of the pipeline. After capturing responses, the pipeline needs to reconstruct the full token sequence for hidden state extraction. The hidden state extraction process (step 2 of the pipeline) feeds the complete conversation — prompt + response — through the verifier model to capture the hidden states at each position. These hidden states become the training targets for the EAGLE-3 draft model.

For this reconstruction to work correctly, the pipeline must insert the proper special tokens. Kimi-K2.5 uses a specific tokenization format:

Why This Message Is a Pivot Point

Message 2918 is not a simple status update. It is a diagnostic intervention that redirects the entire data generation strategy. The user has identified that the pipeline is producing data that would be useless for training — data without reasoning traces is just direct-answer data, which doesn't capture the model's computational process. Training an EAGLE-3 draft model on such data would teach it to predict direct answers, not the reasoning chains that dominate the model's actual generation.

The message also demonstrates a sophisticated understanding of the model architecture. The user knows about the <think> and <response> special tokens, knows that Kimi-K2.5 is designed to produce reasoning before answers, and knows that the reassembly pipeline needs to handle these tokens correctly. This is not surface-level debugging; it's architectural insight.

The Assistant's Response and Its Implications

In the following message ([msg 2919]), the assistant immediately acknowledges both issues and begins investigating. The assistant hypothesizes that the model might not be reasoning because "we're not telling it to" — the prompt format might not include the necessary system prompt or chat template that triggers reasoning mode. The assistant also notes that the vLLM kimi_k2 reasoning parser puts reasoning in reasoning_content, but the OpenAI client might not expose it properly.

The assistant kills the running inference process and begins investigating how to force thinking mode and properly capture reasoning content. This investigation would lead to changes in the prompt format, the client configuration, and the data capture logic.

Assumptions Made and Mistakes Identified

Several assumptions underpin this message and the preceding work:

Assumption 1: The model would automatically reason. The original script assumed that feeding a user question to Kimi-K2.5 would naturally trigger the model's reasoning mode. In reality, reasoning models often require specific prompting — a system message that says "think step by step" or a chat template that includes a reasoning instruction. Without this, the model may skip the reasoning phase entirely and produce direct answers.

Assumption 2: The OpenAI client would capture reasoning. The script assumed that the OpenAI-compatible API exposed by vLLM would return reasoning content in a field accessible via the standard client. The initial bug (checking reasoning_content instead of reasoning) suggests confusion about the API's field naming, but the deeper issue is that the model may not be producing reasoning at all.

Assumption 3: High concurrency with long generations would work. The original C=200 concurrency with up to 8K completion tokens caused timeouts because the default 60s client timeout was too short. The assistant had already fixed this (raising to 1800s and reducing to C=128), but the reasoning capture issue remained.

Mistake: Not verifying the data quality early. The pipeline ran for some time before the user checked the output and spotted the empty reasoning field. Earlier verification — perhaps by inspecting the first few samples — would have caught this problem sooner, saving the time spent generating 388 samples with empty reasoning.

Mistake: Assuming the default prompt would trigger reasoning. Kimi-K2.5, like many reasoning models (DeepSeek-R1, QwQ, etc.), requires specific prompting to enter reasoning mode. The chat template or system prompt must include instructions to reason. The original script used a simple [{"role": "user", "content": question}] format without any system message or reasoning trigger.

Input Knowledge Required

To fully understand this message, one needs:

  1. Kimi-K2.5 architecture knowledge: Understanding that it's a reasoning model that produces <think> and <response> tokens, and that the reasoning trace is the primary target for EAGLE-3 training.
  2. EAGLE-3 training pipeline knowledge: Understanding that the training data consists of hidden states extracted from the verifier model at each token position, and that the token sequence must be correctly reconstructed with special tokens for the hidden states to align properly.
  3. vLLM API knowledge: Understanding that vLLM exposes reasoning content through specific API fields, and that the OpenAI client may or may not expose these fields depending on the version and configuration.
  4. The open-perfectblend dataset: Understanding that this is a dataset of questions used for synthetic data generation, and that the goal is to capture the model's actual reasoning on these questions.
  5. The session's history: Understanding that this is the culmination of a multi-segment effort to build an EAGLE-3 pipeline, with previous segments covering profiling, speculative decoding research, library patching, and training validation.

Output Knowledge Created

This message creates several pieces of knowledge:

  1. The reasoning capture is broken: The pipeline is producing data without reasoning traces, making it unsuitable for EAGLE-3 training.
  2. The sample cap should be reduced: From 25K to 10K, reflecting the need to fix the pipeline before scaling up.
  3. The reassembly logic needs special token handling: The <think> and <response> tokens must be inserted correctly during the hidden state extraction phase.
  4. The model may not be reasoning at all: The short completion lengths (280 tokens for a math problem) suggest the model is producing direct answers without internal reasoning.

The Thinking Process Visible in the Message

The user's thinking process is remarkably clear in this message. They are monitoring the pipeline output in real time, examining the raw JSON records, and comparing them against expectations. The process goes:

  1. Check the sample count: "Let's cap at 10k samples" — the user has been tracking progress and decides 10K is sufficient for training, rather than the originally planned 25K. This is a practical judgment: 10K samples with correct reasoning traces will be more valuable than 25K with broken traces.
  2. Examine the data quality: The user looks at the actual JSON output and immediately spots the empty reasoning field. This requires understanding what the field should contain and recognizing that an empty string is wrong.
  3. Compare against expectations: The user knows that Kimi-K2.5 should produce reasoning for math problems. The fact that the model is producing direct answers (with step-by-step solutions but no internal reasoning) indicates something is wrong with the prompting or capture.
  4. Plan ahead for reassembly: The user anticipates the next pipeline stage and flags the special token handling. This shows end-to-end thinking — not just fixing the immediate bug, but ensuring the downstream stages will work correctly once the fix is applied.

Conclusion

Message 2918 is a textbook example of effective debugging in a complex ML pipeline. It identifies a critical data quality issue early, provides concrete evidence, and offers forward-looking guidance for the fix. The empty reasoning problem would have rendered the entire EAGLE-3 training pipeline ineffective — training on direct answers would produce a draft model that can't predict the model's reasoning traces, which is where most of the computational "effort" lives.

The message also demonstrates the importance of human oversight in automated data generation pipelines. The assistant had written the script, fixed the timeout issues, and restarted the run, but it was the user who spotted that the data was fundamentally wrong. This highlights a key dynamic in AI-assisted development: the assistant can execute and iterate quickly, but the human brings domain knowledge and quality judgment that catches subtle but critical problems.

The fix that followed — investigating how to force thinking mode and properly capture reasoning content — would ultimately produce the high-quality training data needed for the EAGLE-3 draft model. But that work only happened because the user stopped to check the output and identified the problem before the pipeline ran to completion on 25K samples.