---
type: Research concept
title: Recursive Synthetic Data Training
description: >-
  Concept page for training models repeatedly on data generated by earlier
  models.
tags:
  - en
  - research
  - concept
  - research-concept
  - synthetic-data
timestamp: '2026-05-27T17:56:27+08:00'
modified: '2026-08-09T18:32:45.787Z'
content_hash: 'sha256:ba2fdb06ebad4c9b4ef0a71c4f71f77f0be438a2c6dac3237f1f392771cd61c6'
reviewed_at: '2026-08-10T02:34:00+08:00'
review_due: '2027-02-05'
name: Recursive Synthetic Data Training
summary: >-
  Concept page for training models repeatedly on data generated by earlier
  models.
occupation: Research concept
---
**Recursive Synthetic Data Training** is a training process in which generated data from one model generation become part of the training set for a later generation. The process can be intentional, as in self-training or synthetic-data bootstrapping, or incidental, as generated content enters future training corpora.[^recursive]

## Role in this wiki

This page explains the process behind [[Model_Collapse|model collapse]]. It is distinct from synthetic data in general: a one-time synthetic augmentation may be useful, while repeated reuse can amplify distributional errors. The wiki uses this page to separate the mechanism from the outcome. Recursive training is the loop; collapse is one possible degenerative result.

## Connection to Qiao's work

[[When_Sample_Selection_Bias_Precipitates_Model_Collapse|When Sample Selection Bias Precipitates Model Collapse]] studies recursive training under local sample-selection bias. The paper's setting is especially relevant to [[AI_and_Networks|AI and networks]] because the data process is distributed and often low-resource: parties may see different data, select different samples, and only share limited signals. Recursive synthetic-data training therefore becomes a cross-silo reliability problem, not only a generative-model problem.

## See also

- [[Synthetic_Data]]
- [[Model_Collapse]]
- [[Sample_Selection_Bias]]
- [[Data_Silos]]

[^recursive]: Shumailov et al., ["AI models collapse when trained on recursively generated data"](https://www.nature.com/articles/s41586-024-07566-y), *Nature* 631 (2024), study this recursive framing and show that indiscriminate reuse can progressively erase information from the original distribution; their experiments also show less degradation when some original data are retained.
