specific method · filed under data curation

Continuous contribution-score ranking

Retained documents are sorted by a continuous contribution score and allocated according to that ranking rather than fully filtered by quality.

source: 1
model: 1
lab adopt it: 1
strongest: core

How sources treat it

One count per evidence span, weakest treatment to strongest.

core 1

Documented in

Laguna-S-2.1

Evidence

1 span quoted from the sources, strongest treatment first.

For the remaining documents, quality is treated as a ranking signal rather than fully filtered out. Documents are then sorted by a continuous contribution score, allowing us to fill data allocation quota by this ranking.

coredata curationin Laguna XS.2Poolside

laguna m1 xs2 technical report·Poolside

Filed alongside

Other methods under data curation :: data filtering.

CSAM filtering Sensitive data filtering CBRN pre-training data filtering Filtering batched auto-generated and templated content Heuristic filtering Keyword- and regex-based filtering Model-based refusal and inability filtering Pass@100 filtering Pathological repetition filtering SmolVLM-based image-text quality scoring Unified data filtering pipeline Automated verification Coding-agent session filtering and trajectory deduplication Conservative model-based noise filtering Content quality and safety filtering Data cleaning Data filtering pipeline Dense annotation of ambiguous low-quality data Domain filtering with heuristics, classifier scoring, and deduplication Evidence-based data cleaning and training constraints Filtering low-information model-generated content Heuristic and statistical filtering with deduplication and quality models Image-text relevance filtering Long-context data cleaning pipeline