implementation detail · filed under data curation
Model-based refusal and inability filtering
A model identifies and removes samples where a response indicates inability or refusal to solve the problem.
- sources
- 2
- model
- 1
- lab adopt it
- 1
- strongest
- used
How sources treat it
One count per evidence span, weakest treatment to strongest.
used 2
Documented in
Evidence
2 spans quoted from the sources, strongest treatment first.
Subsequently, we employed the DeepSeek-V32-Exp model to identify and remove samples in which the model explicitly indicated an inability or refusal to solve the problem.
usedevaluation onlyin DeepSeek-V3.2DeepSeek
we employed the DeepSeek-V32-Exp model to identify and remove samples in which the model explicitly indicated an inability or refusal to solve the problem
usedunclearin DeepSeek-V3.2-SpecialeDeepSeek
Filed alongside
Other methods under data curation :: data filtering.
CSAM filteringSensitive data filteringCBRN pre-training data filteringFiltering batched auto-generated and templated contentHeuristic filteringKeyword- and regex-based filteringPass@100 filteringPathological repetition filteringSmolVLM-based image-text quality scoringUnified data filtering pipelineAutomated verificationCoding-agent session filtering and trajectory deduplicationConservative model-based noise filteringContent quality and safety filteringContinuous contribution-score rankingData cleaningData filtering pipelineDense annotation of ambiguous low-quality dataDomain filtering with heuristics, classifier scoring, and deduplicationEvidence-based data cleaning and training constraintsFiltering low-information model-generated contentHeuristic and statistical filtering with deduplication and quality modelsImage-text relevance filteringLong-context data cleaning pipeline