beyoru/ox-alpha-coding-reasoning-preview

Ox Alpha Coding Reasoning (preview)

Raw chain-of-thought traces on coding prompts, generated with stealth/ox-alpha through OpenRouter and filtered down to the rows where the model actually thought.

This is a preview slice, not the finished dataset. Generation is still running.

Why this exists

stealth/ox-alpha returns its reasoning unsummarized. That is unusual — most hosted reasoning models either hide the CoT or replace it with a post-hoc summary. Four checks confirmed it is the real trace:

1. Hidden arithmetic. Asked for 83729 * 45193 * 7 with "reply with ONLY the number", the CoT contained every intermediate (83729*45 = 3,767,805, 997*355 = 353935) and a self-check line, while the answer was just the digits. None of those intermediates appear in the output, so they cannot have been reconstructed by a summarizer.
2. Execution tracing. Given a Python loop and asked for the final integer only, the CoT held all nine iterations plus a mid-sentence self-correction (... wait return a after loop ends).
3. Texture. Long traces carry dead ends, hedges, and recall attempts (Actually I recall: in fabric v6/v7, there was a commit ...), plus planning notes for the answer (Write the final solution rigorously with lemmas.).
4. Streaming. Reasoning arrives token-by-token, averaging 5.8 characters per delta, interleaved ahead of the content in the same stream.

Filtering

The teacher uses an adaptive thinking budget: on easy prompts it emits little or no reasoning at all. Roughly 68% of raw generations were therefore discarded.

stage	rows
generated	700
dropped, reasoning under 500 chars	389
dropped, no reasoning emitted	79
dropped, truncated before finishing	4
dropped, empty answer	2
kept	226

Kept rows also require finish_reason == "stop" and a non-empty answer.

Reasoning length among kept rows: min 504, median 2,869, mean 8,779, max 110,054 characters.

domain	rows
algorithmic_reasoning	81
repository_engineering	67
general_implementation	45
debugging	11
c_cpp_systems	7
sql_databases	3
refactoring_optimization	3
ml_data_engineering	2
backend_api	2
javascript_typescript_frontend	2
java_csharp_apps	1
testing	1
rust_go	1

Domain predicts trace length sharply, which makes it a cheap pre-filter: skipping the low-CoT domains avoids spending generations on prompts the teacher answers without thinking. repository_engineering (regressions in real repositories) is an order of magnitude above everything else. Medians over all 700 raw generations, before filtering:

domain	raw rows	median reasoning chars
repository_engineering	72	7,892
c_cpp_systems	15	442
algorithmic_reasoning	197	378
testing	8	228
refactoring_optimization	18	202
backend_api	11	189
debugging	50	187
code_review_explanation	1	186
rust_go	8	182
sql_databases	11	168
javascript_typescript_frontend	10	151
shell_docker_cicd	2	122
ml_data_engineering	12	121
general_implementation	281	108
java_csharp_apps	4	102

Schema

field	description
id	seed id, inherited from the prompt source
domain	task domain label from the prompt source
messages	the prompt, OpenAI chat format
reasoning	raw CoT, exactly as returned
answer	final response
messages_think	messages plus an assistant turn with <think>…</think> inlined, ready for SFT
reasoning_chars	length of reasoning
completion_tokens	reported by the API

Generation config

setting	value
teacher	stealth/ox-alpha (OpenRouter)
reasoning effort	high
max tokens	32,768
temperature	provider default
concurrency	4

Prompt source

Prompts are the input field of trjxter/Kimi-K2.7-CodingTraces-9000x, reused here as seeds. Only the prompts were taken; every reasoning trace and answer in this dataset was generated fresh. Credit for the prompt collection belongs to that dataset's author.

Caveats

• The teacher's identity beyond the ox-alpha label is undisclosed. It self-reports as "ox-alpha, developed by an undisclosed organization" and does not claim any other identity, including inside its own reasoning.
• usage.completion_tokens_details.reasoning_tokens is reported as 0 by the API even when reasoning text is present, so token-level accounting of the CoT is not available.
• No correctness verification has been run on the answers. Traces are unfiltered for factual accuracy — the only filter applied is reasoning length.
• Preview size. Treat it as a sample of the generation distribution, not a training corpus.