DataFlex turns LLM training data strategy into four composable, mid-run-adjustable ops
Puzzleheaded_Box2842 · reddit · 2026-10-15
Most training pipelines fix datasets, mixing ratios, and sampling rules before a run, but the usefulness of examples shifts as the model learns. The author presents OpenDCAI/DataFlex, a data strategy layer built on LLaMA-Factory that decomposes data control into four composable operations:
- Select: choose which examples to train on next
- Mix: adjust domain/source proportions
- Weight: change per-example loss contribution
- Reorder: control when examples appear, including curriculum schedules
Strategies are configured declaratively and custom ones added via a registry, making it cheap to swap or combine approaches. The author also raises open questions: which training feedback signals justify their collection cost, and how often should the data policy change?
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