specific method · filed under data curation
Dynamic sampling for mixed-task reinforcement learning
Uses a dynamic sampler alongside a Data Scheduler to target a specified distribution across data sources.
Also called Dynamic sampling for mixed-task RL, dynamic sampler.
- source
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- models
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- lab adopt it
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How sources treat it
One count per evidence span, weakest treatment to strongest.
used 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
we have maintained a Data Scheduler that targets a specified training distribution across data sources, together with dynamic sampler and partial rollout
usedunclearin MiMo-V2.6 RL and OPD infrastructureXiaomi
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 datasetsFiltering, 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