specific method · filed under data curation
AutoMixer
Replaces manually designed mixtures with automated optimization; the described approach trains proxy models on differing data compositions.
Also called Automated data-mixture optimization.
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How sources treat it
One count per evidence span, weakest treatment to strongest.
used 1core 1
Documented in
Evidence
2 spans quoted from the sources, strongest treatment first.
AutoMixer operates by training a swarm of proxy models, each trained on a different data composition.
coredata curationin Laguna XS.2Poolside
We replaced static manually designed mixtures with a custom automated data mixture process.
useddata curationin Laguna XS.2Poolside
Filed alongside
Other methods under data curation :: data mixture & curriculum.
Deterministic 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 learningFiltering, deduplication, and difficulty calibrationGaussian-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