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 51% of raw generations were discarded — down from 68% in earlier revisions, because generation is now ordered to favour the domains that actually elicit long reasoning.

stage	rows
generated	1,557
dropped, reasoning under 500 chars	643
dropped, no reasoning emitted	143
dropped, truncated before finishing	10
dropped, empty answer	4
kept	757

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

Reasoning length among kept rows: min 504, median 4,329, mean 9,969, max 124,342 characters.

domain	rows
repository_engineering	503
algorithmic_reasoning	127
general_implementation	78
debugging	17
c_cpp_systems	8
refactoring_optimization	5
sql_databases	4
backend_api	4
ml_data_engineering	3
testing	3
javascript_typescript_frontend	2
java_csharp_apps	1
rust_go	1
code_review_explanation	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 1,557 raw generations, before filtering:

domain	raw rows	median reasoning chars
repository_engineering	578	5,218
algorithmic_reasoning	298	391
c_cpp_systems	20	278
ml_data_engineering	20	213
testing	13	201
refactoring_optimization	26	200
backend_api	18	190
debugging	72	187
code_review_explanation	3	186
rust_go	13	182
sql_databases	14	168
javascript_typescript_frontend	15	127
shell_docker_cicd	5	122
general_implementation	454	106
java_csharp_apps	8	84

Only repository_engineering clears the 500-character filter on its median. Every other domain sits below it, so most of their rows are dropped no matter how many are generated.

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	12
prompt order	domains sorted by measured median CoT length, highest first

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.