specific method · filed under data curation
Filtering, deduplication, and difficulty calibration
Filters and deduplicates synthesized data and calibrates its difficulty to support data quality and curriculum balance.
- source
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- model
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- lab adopt it
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- strongest
- core
How sources treat it
One count per evidence span, weakest treatment to strongest.
core 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
(iii) applies rigorous filtering, deduplication, and difficulty calibration to ensure data quality and curriculum balance.
coreunclearin DeepSeek-V4.1-FlashDeepSeek
Filed alongside
Other methods under data curation :: data mixture & curriculum.
AutoMixerDeterministic pre-splitting of ultra-long documentsVulnerability-discovery data inclusionAgent-centric multimodal data mixtureAttribution, hedging, and refusal data subsetsBlind pairwise bucket-boundary calibrationData SchedulerDomain-specific multimodal datasetsDynamic sampling for mixed-task reinforcement learningGaussian-based data mixture and curriculum constructionInterleaved dataInterleaved multimodal trainingKL-regularized data-mixture optimizationLong-context data upsamplingPass-rate-based RL task samplingPrior-constrained Dirichlet mixture explorationProtocolQA-aligned RL training datasetsSample MixerScaling-ladder-guided data and model planningSmooth weighted round-robin source schedulingText-only pre-training corpusThree-stage data mixture strategyThree-stage pre-training curriculumTwo-phase curriculum