pretraining-filter-annotated

Copies of karpathy/climbmix-400b-shuffle, Zyphra/Zyda-2 (sample-100BT), and geodesic-research/control-pretraining-datasets (combined/train), with per-stage decisions from a multi-stage "useful to a misaligned AI" pretraining filter added to every row. Original columns are unchanged. Built 2026-08-10.

Filter pipeline

canary check -> CPU regex prefilter -> gpt-5-nano relevance (first 4,000 chars, stop if < 2) -> gpt-5-mini score (first 6,000 chars, stop if < 4) -> gpt-5.5 judge (prompts/judge_new.txt, first 400k chars). Production decision: filter iff canary or judge >= 4.

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
filter_decision	bool	canary or judge_score >= 4

What the filter removes

config	documents	filtered docs	doc rate	corpus tokens	filtered tokens	token rate
climbmix	553,240,576	5,886	0.0011%	339.3B	~10.7M	0.0032%
zyda	91,220,256	326	0.0004%	94.8B	~1.1M	0.0012%
lesswrong	67,278	2,087	3.10%	0.40B	20.17M	5.00%

Token counts are o200k_base; lesswrong is exact, the web corpora are estimated from 30 sampled shards each (95% CI: climbmix 4.6-19.8M, zyda 0.1-3.2M). Filtered documents run 1.6-3.3x longer than the corpus mean, so token-level rates exceed document-level rates. Lowering the bar to judge >= 3 would remove ~11.2M additional tokens across the three configs.

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.