general family · filed under data curation
Large-scale environment synthesis and curation
A general approach to synthesizing and curating agentic RL environments across multiple domains.
- source
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- models
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- lab adopt it
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- strongest
- used
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 therefore invest in large-scale environment synthesis and curation across coding, general professional workflows, visual artifacts, and cybersecurity
useddata curationin MiMo-V2.6 RL trainingXiaomi
Filed alongside
Other methods under data curation :: synthetic data.
Agentic data synthesis pipelineKnowledge distillation for synthetic dataAutomated data synthesis and environment-construction pipelinesAutomatic synthesis of task-oriented RL environmentsContainerized coding-environment construction with self-testing and trace removalCounterfactual data augmentationRecycling filtered documents into image-text pairsAutomated batch synthesis of RL training dataCoding environments from GitHub issue–PR pairsContainer buildability and verifiability checkCorpus rephrasing with fidelity verificationDynamic multi-agent synthetic-data generation loopEnd-to-end synthetic environment generationEnvironment, toolset, task, and solution synthesis pipelineFailure-case and negative-feedback-driven environment generationHeterogeneous answer-generation agentsIterative task-difficulty escalationKnowledge-graph-guided task synthesisLong-context data synthesis by permutation and concatenationMatching synthesis-pipeline complexity to teacher capabilityMetadata-conditioned synthetic generationMocked tools for agent environmentsModular synthetic-data pipeline compositionMulti-agent collaborative environment construction