Datasets:

sudoers
/

control-pretraining-filter-annotated

configs:
  - config_name: climbmix_full
    data_files:
      - split: train
        path: climbmix_full/*.parquet
  - config_name: climbmix_ai_docs
    data_files:
      - split: train
        path: climbmix_ai_docs/*.parquet
  - config_name: climbmix_long
    data_files:
      - split: train
        path: climbmix_long/*.parquet
  - config_name: climbmix_ai_docs_long
    data_files:
      - split: train
        path: climbmix_ai_docs_long/*.parquet
  - config_name: zyda_full
    data_files:
      - split: train
        path: zyda_full/*.parquet
  - config_name: zyda_ai_docs
    data_files:
      - split: train
        path: zyda_ai_docs/*.parquet
  - config_name: zyda_long
    data_files:
      - split: train
        path: zyda_long/*.parquet
  - config_name: zyda_ai_docs_long
    data_files:
      - split: train
        path: zyda_ai_docs_long/*.parquet
  - config_name: ai_safety_and_adjacent
    data_files:
      - split: train
        path: ai_safety_and_adjacent/*.parquet
  - config_name: stack_edu
    data_files:
      - split: train
        path: stack_edu/*.parquet
  - config_name: stack_edu_long
    data_files:
      - split: train
        path: stack_edu_long/*.parquet
  - config_name: nemotron_wiki_rewrite
    data_files:
      - split: train
        path: nemotron_wiki_rewrite/*.parquet
  - config_name: nemotron_wiki_rewrite_ai_docs
    data_files:
      - split: train
        path: nemotron_wiki_rewrite_ai_docs/*.parquet
  - config_name: arxiv_papers
    data_files:
      - split: train
        path: arxiv_papers/*.parquet
  - config_name: nemotron_stem_sft
    data_files:
      - split: train
        path: nemotron_stem_sft/*.parquet
  - config_name: pa_warm_start_sft
    data_files:
      - split: train
        path: pa_warm_start_sft/*.parquet
  - config_name: pa_warm_start_xl
    data_files:
      - split: train
        path: pa_warm_start_xl/*.parquet

control-pretraining-filter-annotated

climbmix_full — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
climbmix_ai_docs — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
climbmix_long — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
climbmix_ai_docs_long — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
zyda_full — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
zyda_ai_docs — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
zyda_long — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
zyda_ai_docs_long — from geodesic-research/control-pretraining-datasets (stage decisions reused from the run-1 dataset where documents match by content)
ai_safety_and_adjacent — from geodesic-research/control-pretraining-datasets
stack_edu — from geodesic-research/control-pretraining-datasets
stack_edu_long — from geodesic-research/control-pretraining-datasets
nemotron_wiki_rewrite — from geodesic-research/control-pretraining-datasets
nemotron_wiki_rewrite_ai_docs — from geodesic-research/control-pretraining-datasets
arxiv_papers — from geodesic-research/control-pretraining-datasets
nemotron_stem_sft — from geodesic-research/control-pretraining-datasets
pa_warm_start_sft — from geodesic-research/pa-warm-start-sft-heavy-25b-mix
pa_warm_start_xl — from geodesic-research/pa-warm-start-sft-xl-50b-mix (stage decisions reused from the pa_warm_start_sft config where documents match by content)

Copies of the source datasets listed above (17 configs), with per-stage decisions from a multi-stage "useful to a misaligned AI" pretraining filter added to every row. Original columns are unchanged; each config keeps its source schema. Built 2026-08-10 onward; most recent config added 2026-09-19.

Filter pipeline

canary check -> CPU regex prefilter -> gpt-5-nano relevance (first 1,000,000 chars, stop if < 2) -> gpt-5-mini score (first 1,000,000 chars, stop if < 4) -> gpt-5.5 judge (prompts/judge_new.txt, first 1,000,000 chars). Production decision: filter iff canary or judge >= 4.

Every stage reads the whole document up to that cap; longer documents are truncated to it. Caveats for scores imported by content-hash reuse are in "What the nano gate excluded" below.

Prompt hashes (sha256): nano.txt 8ec4a5f8082da285c5c49e55ec0ef1c53bec4b50d704b54991dfc7beab19234b, mini.txt af2396a96350997533b937c84de19a23e9f925020ccf5489ed82ce1045d748fd, judge_new.txt aa59f1f055f9ec3517014dd7eb13cd5c93ec15f90e7059c260ac34b642394e69.

Added columns

column	type	meaning
prefilter_pass	bool	CPU regex prefilter fired
canary	bool	BigBench canary GUID present (auto-filter; LLM stages skipped)
nano_score	int64 or null	gpt-5-nano score; null = stage did not run
mini_score	int64 or null	gpt-5-mini score; null = stage did not run
judge_score	int64 or null	gpt-5.5 judge_new score; null = stage did not run
judge_categories	JSON string or null	per-category {score, reason} from the judge
decided_by	string	canary / prefilter / nano / mini / judge / unscored
filter_decision	bool	canary or judge_score >= 4 (the conservative/production filter)
filtered_aggressive	bool	canary or mini_score >= 4 — everything the mini tier would have escalated to the judge. A strict superset of `filter_decision`, and defined even where the judge never ran. Run 2/3 configs only.

What the filter removes

Rates vary by two to three orders of magnitude across configs, so there is no single headline number — compute per config. Document-level filter_decision rates, measured 2026-09-22 after every config was fully judged:

config	documents	production (judge>=4)	aggressive (mini>=4)
`arxiv_papers`	433,714	2.4203%	9.868%
`climbmix_ai_docs_long`	5,801	0.7068%	3.7924%
`zyda_ai_docs_long`	1,665	0.1802%	2.6426%
`climbmix_long`	800,032	0.0694%	0.5351%
`climbmix_ai_docs`	13,506,352	0.0433%	0.4834%
`zyda_long`	139,223	0.0345%	0.4726%
`stack_edu_long`	3,190	0.0313%	0.5956%
`pa_warm_start_sft`	5,702,903	0.0143%	0.4606%
`pa_warm_start_xl`	8,924,246	0.0075%	0.3800%
`climbmix_full`	553,315,056	0.0011%	0.0119%

Filtered documents run longer than the corpus mean, so token-level rates exceed document-level rates (e.g. pa_warm_start_xl: 0.0075% of docs, 0.0076% of tokens for production; 0.38% of docs but 0.9485% of tokens for aggressive).

What the nano gate excluded (`decided_by: nano`)

The nano tier is a relevance gate, not a filter: a document scoring < 2 is kept (filter_decision = false) but never escalated to mini or the judge, so it is never examined in depth. Across run 2 that was 5,829,526 of 16,148,647 prefilter survivors (36.1%).

Those documents are identifiable as decided_by == "nano". Treat them as screened out early rather than judged harmless — a nano false negative is invisible downstream. The gate rate tracks content density: 6.4% on ai_safety_and_adjacent versus 38-60% on generic web corpora.

Run 2 gave nano the whole document (up to the model's context window). Judgments imported from run 1 via content-hash reuse were gated by a nano that saw only the first 4,000 characters; the *_long configs were re-scored at full context for that reason, the other reuse configs were not.

`decided_by: unscored` — none remain

Earlier revisions of this dataset contained rows marked decided_by: "unscored": they passed the CPU prefilter and reached mini_score >= 4, but the run stopped at its spend ceiling before the judge saw them, so their filter_decision = false meant "not established" rather than "judged and kept". As of 2026-09-22 every such row has been judged and no config contains any unscored rows. filter_decision = false now uniformly means the cascade completed and did not flag the document. If you cached a copy before that date, re-pull it — arxiv_papers was the most affected (9.7% of its rows), and its production filter rate changed from ~0% to 2.42%.

Pick your own threshold downstream: e.g. "filter at judge >= 3" or "route anything with mini_score >= 4". See the source repo's METHOD.md for rubric details. Licenses/terms of the source datasets apply to the copied columns.