Bridging Two Worlds: Reconstructing Native Token Sequences from OpenRouter API Responses

In the middle of a complex pipeline to generate training data for an EAGLE-3 speculative decoding drafter, the assistant reaches a critical inflection point. Message [msg 4017] captures a moment of transition — the assistant has just finished a deep investigation into the Kimi K2.5 tokenizer's quirks and is now pivoting to understand how OpenRouter's API will return model outputs. The message is brief but pivotal: it summarizes what has been learned about the native token format, identifies the key unknown about OpenRouter's response structure, and dispatches a targeted documentation query to resolve that unknown.

The Broader Mission: Building EAGLE-3 Training Data

To understand why this message matters, we need to step back. The session is part of a larger effort to train an EAGLE-3 speculative decoding drafter for the Kimi K2.5 model. EAGLE-3 is a technique where a lightweight "draft" model predicts multiple future tokens in parallel, allowing the main model to verify them in a single forward pass — dramatically speeding up inference. The drafter must be trained on the exact token-by-token outputs of the base model, because speculative decoding requires precise alignment between what the draft model predicts and what the base model would generate.

Initially, the assistant was generating training data using a local SGLang server running on a machine with 8 RTX PRO 6000 Blackwell GPUs. This gave direct access to output_ids — the exact token sequences the model generated. But local inference was resource-intensive and slow for the scale needed (tens of thousands of samples). The pivot to OpenRouter API promised faster throughput and lower cost, but introduced a fundamental problem: OpenRouter returns text, not token IDs. The assistant must reconstruct the exact token sequence from the text response, and any mismatch would corrupt the training data.

The Tokenizer Investigation That Preceded This Message

Messages [msg 4013] through [msg 4016] represent a meticulous investigation into the Kimi K2.5 tokenizer. The assistant discovered several critical quirks:

1. <|im_end|> (token ID 163533) decodes to 'chas', not to its textual representation. This means you cannot simply put <|im_end|> in a string and encode it — you'd get token 163586 instead. The raw token ID must be injected directly.
2. <|im_end|> (token ID 163607) roundtrips correctly — encoding the text "<|im_end|>" gives back [163607]. This special token acts as a clean boundary that BPE will not merge across.
3. BPE boundary effects exist — encoding "x" and "y" separately gives two tokens, but encoding "xy" together can give a single merged token. This means the naive approach of encoding reasoning and content separately and concatenating the token lists could produce different results than encoding the full output string.
4. Tool call special tokens (<|tool_calls_section_begin|>, <|tool_call_begin|>, etc.) roundtrip correctly when encoded from their text form. Through careful testing, the assistant verified that <|im_end|> acts as a clean BPE boundary — encoding reasoning_text + "<|im_end|>" + content_text as a single string produces the same token sequence as encode(reasoning_text) + [163607] + encode(content_text). This was confirmed across multiple edge cases including content starting with spaces, newlines, code fences, and other characters that might trigger BPE merges. The assistant also verified that real SGLang model outputs roundtrip perfectly: encode(decode(output_ids)) == output_ids for actual generated samples. This confirmed that the canonical BPE tokenization (encoding the full decoded text) matches what the model actually produced.

What Message 4017 Actually Says

The message opens with a clear statement of what has been learned:

Now I can see exactly how tool calls work in Kimi K2.5's native format:

The assistant then shows the native format as a code block:

<|im_start|>reasoning...<|im_end|>content_text<|tool_calls_section_begin|><|tool_call_begin|>functions.name:0<|tool_call_argument_begin|>{"arg": val}<|tool_call_end|><|tool_calls_section_end|><|im_end|>

This format shows the complete structure of a Kimi K2.5 response that includes tool calls: the reasoning section (wrapped in <|im_start|>...<|im_end|> tags), followed by the content text, followed by the structured tool call section with its special delimiter tokens, and finally the end-of-message token <|im_end|>.

The assistant then identifies the critical question:

The key question is: how does OpenRouter return this? OpenRouter's chat completions API typically separates content and tool_calls. The tool_calls would be returned in the message.tool_calls array, and the content text before the tool calls would be in message.content. The special tokens like <|tool_calls_section_begin|> would be stripped.

This is the core tension. The native Kimi K2.5 format embeds tool calls inline as special tokens within a single text stream. OpenRouter (following the OpenAI API convention) separates tool calls into a structured JSON array. The assistant must reverse this transformation: given the API response with separated content and tool_calls, it must reconstruct the exact token sequence that the model would have generated natively, including all the special delimiter tokens.

The message concludes with:

I need to reconstruct the exact token sequence from whatever OpenRouter gives me. Let me check what the OpenRouter response looks like for a tool-calling model:

And then makes an exa_get_code_context_exa call — a web/documentation search tool — to look up OpenRouter's tool calling documentation.

The Reasoning and Decision-Making Process

The thinking visible in this message reveals a methodical approach to a complex reconstruction problem. The assistant has already:

1. Characterized the native format through empirical testing with the actual tokenizer
2. Identified the mapping problem — OpenRouter's structured format vs. the native inline format
3. Formulated a hypothesis about how OpenRouter returns tool calls (separated into tool_calls array, special tokens stripped)
4. Decided to verify this hypothesis by consulting OpenRouter's documentation The decision to look up documentation rather than make assumptions is significant. The assistant could have guessed the format and written code to handle it, but the cost of a mistake would be high — corrupted training data that produces a defective EAGLE-3 drafter. Instead, the assistant invests effort in getting the exact specification.

Assumptions and Potential Pitfalls

The message contains several assumptions that deserve scrutiny:

Assumption 1: OpenRouter follows OpenAI's format exactly. This is generally true for the chat completions endpoint, but different providers may implement tool calling differently. Some providers might not support tool calling for Kimi K2.5 at all, or might pass the raw text through without parsing.

Assumption 2: Special tokens will be stripped from content. The assistant assumes that when OpenRouter parses a tool call, it removes the special delimiter tokens (<|tool_calls_section_begin|>, etc.) from the content text and places the structured data in tool_calls. But this depends on whether the provider recognizes Kimi K2.5's custom tool call format — many providers only support OpenAI's standard tool calling format.

Assumption 3: The model will generate tool calls even without the tools parameter. In subsequent messages ([msg 4018]), the assistant discovers that B5_openthoughts has 100% function/tool mentions in prompts, and begins to question whether tool calls will actually appear in the OpenRouter responses. Since the API request doesn't include the tools parameter, most providers won't activate tool calling mode, and any tool-call-like text the model generates will appear as raw text in content.

Assumption 4: Provider routing will work correctly. The assistant plans to exclude certain providers (Fireworks NVFP4, BaseTen FP4) and route to the cheapest available INT4 provider. This assumes that the routing logic correctly identifies which providers offer INT4 quantization of Kimi K2.5.

Input Knowledge Required

To fully understand this message, a reader needs:

• Understanding of BPE tokenization: How byte-pair encoding works, how special tokens are handled, and why boundary effects matter. The assistant's concern about BPE merging across the <|im_end|> boundary is a subtle point that requires knowledge of how tokenizers segment text.
• Knowledge of the OpenAI chat completions format: The distinction between message.content (text) and message.tool_calls (structured array) is essential. The assistant assumes OpenRouter follows this convention.
• Understanding of speculative decoding and EAGLE-3: Why exact token IDs matter for training a draft model. If the token sequence is off by even one token, the draft model's predictions won't align with the base model's outputs.
• Awareness of the Kimi K2.5 model architecture: Its use of special tokens for reasoning boundaries (<|im_start|>, <|im_end|>) and tool call delimiters.
• Context from the broader session: The pivot from local SGLang inference to OpenRouter API, the scale of data needed (~40K samples, 138M tokens), and the time/cost constraints driving the decision.

Output Knowledge Created

This message creates several important outputs:

1. A clear characterization of the native Kimi K2.5 tool call format, including the exact special tokens and their structural roles.
2. A precise problem statement: the need to reconstruct native token sequences from OpenRouter's structured API responses.
3. A targeted documentation query that will inform the implementation of the reconstruction logic.
4. A foundation for the subsequent implementation in [msg 4020], where the assistant rewrites the generate_one function to handle both the case where OpenRouter returns structured tool calls and the case where tool call text appears raw in content.

The Broader Significance

Message [msg 4017] represents a boundary-crossing moment in the pipeline. The assistant is moving from a controlled environment where it has full access to model internals (token IDs, hidden states) to a mediated environment (OpenRouter API) where it only receives text. The challenge of reconstructing exact token sequences from text is non-trivial — it requires deep understanding of the tokenizer's behavior, careful handling of special tokens, and awareness of how API providers may transform the model's raw output.

This message also illustrates a pattern common in ML engineering work: the need to bridge between research/prototyping environments (where you have full control and visibility) and production/deployment environments (where you have limited visibility and must work through APIs). The assistant's methodical approach — investigate the native format, identify the mapping problem, consult documentation, then implement — is a template for solving this class of problems.

The documentation query that concludes the message sets up the next phase of work. In the messages that follow ([msg 4018] through [msg 4020]), the assistant will discover that OpenRouter does follow the OpenAI format, that some providers may not support tool calling for Kimi K2.5, and that the safest approach is to handle both structured tool calls and raw text tool calls. The reconstruction logic will need to be robust enough to handle multiple provider behaviors, all while producing exact token sequences that match what the model would have generated natively.