configs:
- config_name: climbmix
data_files:
- split: train
path: climbmix/*.parquet
- config_name: zyda
data_files:
- split: train
path: zyda/*.parquet
- config_name: lesswrong
data_files:
- split: train
path: lesswrong/*.parquet
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.