The Case of the Two <|im_end|> Tokens: A Debugging Deep Dive into Token ID Reconstruction for EAGLE-3 Training Data

Introduction

In the course of building a large-scale training dataset for an EAGLE-3 speculative decoding drafter, a seemingly minor token ID mismatch threatened to corrupt every single training sample. This article examines message <msg id=4024> from an opencode coding session, a message that captures the precise moment a critical bug was identified and corrected. The discovery — that the Kimi K2.5 tokenizer contains two different <|im_end|> tokens with distinct token IDs — is a masterclass in the kind of meticulous debugging required when working with large language model tokenizers and their idiosyncratic vocabularies.

The Subject Message

The message reads in full:

The last token is 163586, not 163533! There are TWO different <|im_end|> tokens: - 163533: decodes to "chas" — this is NOT the actual im_end token used by the model - 163586: decodes to "<|im_end|>" — this IS the actual im_end token the model generates

>

I had the wrong token ID for <|im_end|>. Let me verify:

>

[bash script to test both token IDs, check encode/decode roundtrip, examine last tokens in actual datasets, and scan vocabulary range for special tokens]

The message then executes a test script that confirms: token 163586 is the real <|im_end|>, encode("<|im_end|>") returns [163586], both B1_glaive and B3_magicoder datasets consistently use 163586 as their final token, and token 163533 is merely a BPE fragment that happens to decode to the string "chas".

Context and Motivation: Why This Message Was Written

To understand why this message exists, we must step back into the broader pipeline. The assistant was engaged in generating training data for an EAGLE-3 speculative decoding drafter — a system that learns to predict a target model's hidden states to accelerate inference. The pipeline had already completed local GPU-based inference for some datasets (B1_glaive, B2) and was now pivoting to OpenRouter API for datasets B3 through B8, a strategic shift driven by the prohibitive cost and time of running 83,000 prompts through local GPUs.

The critical challenge was token-level fidelity. EAGLE-3 training requires exact token ID sequences, not just decoded text. When using OpenRouter's chat completions API, the response comes as human-readable text — the model's output decoded into strings. The assistant needed to reconstruct the exact token ID sequence from that text, a process fraught with peril because tokenization is not a simple character-level operation. Byte-Pair Encoding (BPE) can produce different tokenizations for the same string depending on context, and special tokens like <|im_end|> and <|/think|> must be injected as raw token IDs rather than encoded from text.

The reconstruction function (reconstruct_output_ids) had been written with a constant IM_END_TOKEN_ID = 163533. This value had been determined earlier in the session, likely by examining the tokenizer's vocabulary or by decoding individual token IDs. The assumption seemed straightforward: token 163533 decodes to something related to <|im_end|>, so it must be the right token.

But when the assistant ran a validation test comparing reconstructed token sequences against ground-truth SGLang outputs, every single sample failed — 100% mismatch rate. The debug script in <msg id=4023> revealed the smoking gun: the last token in actual model outputs was 163586, not 163533. The decoded text ended with "<|im_end|>", not "chas".

The Discovery: Two Tokens, One Name

Message <msg id=4024> is the moment of insight. The assistant realizes that the vocabulary contains two tokens that both relate to <|im_end|>:

• Token 163533: Decodes to the string "chas". This is a BPE subword token — a fragment that happens to appear in the tokenizer's vocabulary because it's a common substring in some language. It is not a special token.
• Token 163586: Decodes to the literal string "<|im_end|>". This is the actual special end-of-response token that the model generates. The confusion is understandable. If you examine the tokenizer's vocabulary by decoding individual IDs, you might find token 163533 and see that it decodes to something that looks like it could be related to <|im_end|> (since "chas" is part of the string "<|im_end|>" when you squint — the "c" from <|, "has" from the middle... actually no, "chas" doesn't appear in <|im_end|> at all). More likely, the assistant had earlier run a scan of the vocabulary for tokens containing "im" or "end" in their decoded form, and 163533 showed up because... wait, it doesn't contain those substrings either. Let me re-examine. Actually, looking at the debug output from <msg id=4023>:

added_tokens_decoder sample:
  163533: 'chas'
  163536: ' Pim'
  163586: '<|im_end|>'
  163587: '<|im_user|>'
  163588: '<|im_assistant|>'
  163591: '<|end_header_id|>'
  163594: '<|im_system|>'
  163596: '<|tool_calls_section_end|>'
  163599: '<|tool_...

The scan filtered for tokens where the decoded text contains "im" or "end" or equals "chas". Token 163533 ("chas") was included because of the d == "chas" filter — it was specifically flagged as a known oddity. The assistant had likely discovered 163533 earlier, decoded it to "chas", and incorrectly assumed this was the <|im_end|> token because "chas" is a substring of the <|im_end|> token string... but it's not. "chas" doesn't appear in <|im_end|>.

This is the key puzzle: why did the assistant initially believe 163533 was the <|im_end|> token? The most likely explanation is that the assistant had previously run a command like tok.decode([163586]) and gotten "<|im_end|>", but then when looking at the vocabulary scan, saw 163533 and confused the two. Or perhaps the assistant had encountered 163533 in some other context — maybe it appeared as a BPE merge artifact when encoding text that contained <|im_end|> — and mistakenly assigned it as the special token ID.

Whatever the origin of the error, the critical point is that the assistant had been working with the wrong constant for the entire reconstruction function, and every single training sample generated through OpenRouter would have had a corrupted token sequence — ending with token 163533 ("chas") instead of 163586 ("<|im_end|>").

The Verification Script

The message doesn't just announce the discovery — it immediately verifies it with a comprehensive test script. The script does four things:

1. Round-trip test: Decodes both candidate tokens, then re-encodes the decoded text. Token 163586 round-trips perfectly: decode([163586]) = "<|im_end|>", encode("<|im_end|>") = [163586]. Token 163533 also round-trips: decode([163533]) = "chas", encode("chas") = [163533]. Both are valid tokens, but only one is the actual special token.
2. Explicit encoding test: tok.encode("<|im_end|>", add_special_tokens=False) returns [163586]. This is definitive — the tokenizer itself maps the string <|im_end|> to token 163586.
3. Empirical validation: The script samples the last tokens from 20 responses in both B1_glaive and B3_magicoder datasets. Every single one ends with token 163586. Zero end with 163533. This confirms that the model consistently uses 163586 as its end-of-response marker.
4. Vocabulary scan: The script iterates token IDs from 163530 to 163610, decoding each and checking for "im", "end", or "chas". This reveals the full family of special tokens in this range: <|im_end|> (163586), <|im_user|> (163587), <|im_assistant|> (163588), <|end_header_id|> (163591), <|im_system|> (163594), <|tool_calls_section_end|> (163596), and others. This verification is thorough and leaves no room for doubt. The assistant doesn't just trust the initial insight — it runs concrete tests that produce unambiguous evidence.

Assumptions and Mistakes

Several assumptions and mistakes are visible in this message and its immediate predecessors:

The mistaken assumption: Token 163533 was the <|im_end|> token. This was likely derived from an earlier vocabulary scan where the assistant saw "chas" and somehow connected it to <|im_end|>. The connection is tenuous — "chas" is not a substring of <|im_end|> — suggesting the error may have been a simple copy-paste mistake or a misreading of the vocabulary dump.

The assumption that decode-and-strip would work: In <msg id=4022>, the reconstruction function stripped the decoded text of im_end_text = tok.decode([IM_END_TOKEN_ID]) which was "chas". The debug in <msg id=4023> showed that the actual decoded output ended with "<|im_end|>", not "chas", so the stripping logic never triggered — the content still contained "<|im_end|>" as text, and when re-encoded, it produced token 163586, but then the reconstruction also appended 163533 as the final token, resulting in one extra token.

The assumption about BPE context independence: The assistant initially assumed that tok.decode([163533]) would produce the same string regardless of surrounding tokens. The debug in <msg id=4023> showed this is false — when decoded in context with preceding tokens, token 163586 contributes "<|im_end|>" to the output, but when decoded alone it also produces "<|im_end|>". The real issue was simply having the wrong token ID.

The assumption that the vocabulary scan was correct: The earlier scan that produced the list of special tokens may have been misleading. The assistant had likely run a command like for tid in range(163530, 163610): d = tok.decode([tid]); if "im" in d.lower() or "end" in d.lower(): print(tid, d) and gotten both 163533 and 163586 in the results. Without careful inspection, it would be easy to grab the wrong one.

Input Knowledge Required

To fully understand this message, one needs:

1. Understanding of BPE tokenization: Byte-Pair Encoding tokenizers can have multiple tokens that decode to similar-looking strings. Special tokens are typically added to the vocabulary and may coexist with naturally-occurring BPE subword tokens that happen to look similar.
2. Knowledge of the Kimi K2.5 model architecture: The model uses a specific set of special tokens for chat formatting, including <|im_end|>, <|im_start|>, <|/think|>, and tool-call-related tokens. These are in the 163530-163610 range of the vocabulary.
3. Understanding of the EAGLE-3 training pipeline: The assistant is generating training data where exact token ID sequences must be preserved. Text-based APIs like OpenRouter return decoded text, so reconstruction requires careful handling of special tokens as raw IDs rather than encoded text.
4. Familiarity with the OpenRouter API: The API returns responses in OpenAI-compatible format with message.content and optionally message.reasoning fields. The reconstruction function must handle both cases.
5. Knowledge of the SGLang inference engine: The ground-truth data was generated by SGLang, and the validation tests compare reconstructed sequences against SGLang-produced token IDs.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

1. The correct token ID for <|im_end|> is 163586, not 163533. This is immediately actionable — the reconstruction function must be updated.
2. Token 163533 is a BPE subword token that decodes to "chas" and is unrelated to the special token system. It should not be used as an end-of-response marker.
3. The model consistently uses 163586 as its final token across different datasets (B1_glaive, B3_magicoder), confirming it's the universal end-of-response marker.
4. The vocabulary range 163530-163610 contains many special tokens including <|im_user|> (163587), <|im_assistant|> (163588), <|end_header_id|> (163591), <|im_system|> (163594), <|tool_calls_section_end|> (163596), and <|tool_call_end|> (163599). This provides a reference for any future special token handling.
5. The encode/decode round-trip is reliable for these special tokens — encoding the decoded text produces the original token ID, confirming that the tokenizer correctly maps the string representation to the special token.

The Thinking Process

The reasoning visible in this message and its predecessors reveals a methodical debugging approach:

Step 1: Hypothesis formation. After the validation test showed 100% mismatch, the assistant hypothesized that the issue was with how the <|im_end|> token was being handled — specifically, that stripping "chas" from the decoded text wasn't working because the decoded text didn't actually end with "chas".

Step 2: Targeted debugging. The debug script in <msg id=4023> examined the last 5 tokens of a sample response, decoded each individually, and compared the full decoded text against expectations. This immediately revealed that the last token was 163586 (decoding to "<|im_end|>"), not 163533 (decoding to "chas").

Step 3: Insight and reframing. In <msg id=4024>, the assistant reframes the problem: "There are TWO different <|im_end|> tokens." This is a conceptual leap — recognizing that the vocabulary contains multiple tokens that could be confused, and that the assistant had simply chosen the wrong one.

Step 4: Comprehensive verification. Rather than immediately fixing the code, the assistant writes a thorough test script that checks encode/decode round-trips, explicit encoding, empirical last-token distributions, and vocabulary scanning. This ensures the discovery is correct before any code changes are made.

Step 5: Documentation of the finding. The message itself serves as documentation — it clearly states the two token IDs, their decoded values, and which one is correct. This creates a permanent record that can be referenced later.

Broader Implications

This bug, while seemingly minor, could have had significant downstream consequences. Every training sample generated through OpenRouter would have ended with token 163533 ("chas") instead of 163586 ("<|im_end|>"). During EAGLE-3 training, the model learns to predict the target model's hidden states at each token position. A corrupted end-of-sequence token would:

1. Shift all token alignments between the draft model and target model, since the sequence would be one token longer than expected.
2. Introduce a spurious "chas" token at the end of every sequence, which the drafter would learn to predict, wasting model capacity.
3. Potentially confuse the loss calculation if the loss function treats the end-of-sequence token specially.
4. Make the trained drafter produce "chas" at the end of every generation, which would then need to be decoded and stripped — a silent degradation that might go unnoticed in throughput benchmarks. The fact that the assistant caught this bug before running the full OpenRouter inference pipeline (which was about to generate tens of thousands of samples) saved hours of compute time and prevented the corruption of the entire training dataset.

Conclusion

Message <msg id=4024> captures a classic debugging moment in machine learning engineering: the discovery that a seemingly straightforward constant is wrong, and the methodical verification that follows. The case of the two <|im_end|> tokens illustrates a general principle when working with LLM tokenizers: never assume a token ID based on its decoded string alone. Always verify by encoding the string and checking the round-trip, and always validate against actual model outputs rather than relying on vocabulary introspection.

The assistant's approach — forming a hypothesis, writing a targeted debug script, recognizing the pattern, and then comprehensively verifying — is a model of effective debugging. The message also demonstrates the importance of documenting discoveries clearly, creating a reference that can be consulted later when similar issues arise.

For anyone working with custom tokenizers, fine-tuning LLMs, or building training pipelines that require token-level fidelity, this message serves as a cautionary tale: the tokenizer's vocabulary may contain traps, and the only way to be sure is to test against real data.