mlsanityData to 14 Oct 2026

CuratorKIT : Data Curation and Synthetic Data Generation for LLM Post-Training

arXiv 2606.21631 ↗First posted 19 Jun 2026cs.CL cs.LGcited by 1

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Data curation is a critical part of post-training pipelines for large language models, yet existing tools often treat ingestion, deduplication, synthetic generation, and quality filtering as separate stages. This fragmentation makes it difficult to audit pipeline decisions or understand why individual samples are rejected. CuratorKIT is an open-source Python library that covers this full lifecycle in a single configurable pipeline. The framework is composed of six source format readers and automatic schema detection, a pre-generation data hygiene layer for credentials, PII, and toxic content, eight LLM-powered generation tasks, three complementary quality gates with provenance-exact hallucination verification, structured adaptive recovery, and five training-ready export formats compatible with TRL, Unsloth, and AlignTune. Every pipeline decision is recorded in an append-only per-sample provenance chain, and rejected samples carry structured failure reasons rather than being silently discarded. CuratorKIT supports 100+ LLM providers through LiteLLM, exposes both a Python API and a YAML-driven CLI, and is designed for practitioners who need reproducible, auditable data pipelines at scale .

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 13 of its 21 references that are in the map. First posted on arXiv 19 Jun 2026.

Builds on

13 of 21 references.

References · 13

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1 more
  1. 2026Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agentscited by 0

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  1. 2025Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
  2. 2024DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
  3. 2026Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation
  4. 2025Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
  5. 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs
  6. 2026Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
  7. 2024Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates
  8. 2025Watermarking LLM-Generated Datasets in Downstream Tasks