Project Halflight
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What synthetic data does to a model over time.
Overview
Studies the compounding effects of synthetic data on model behavior across successive fine-tuning generations, tracking alignment drift, style collapse, and cognitive degradation.
Research Focus
The Research Problem
Training language models recursively on model-generated synthetic datasets leads to variance reduction, loss of rare domain knowledge, and behavioral homogenization over repeated fine-tuning cycles.
Why It Matters
As web datasets become increasingly saturated with AI-generated text, maintaining data quality and preventing model collapse is essential for long-term AI safety.
Research Objective
Quantify behavioral drift, perplexity degradation, and safety alignment shifts across 10+ successive generations of synthetic self-training.
Technical Approach
Key Methodology Vectors
- Recursive fine-tuning loops using synthetic output datasets across multiple architecture variants.
- Semantic diversity measurement and tail-knowledge evaluation across generations.
- Benchmark testing against standard reasoning and safety evaluation suites.
Expected Outcomes & Milestones
- Data curation heuristics and hybrid synthetic-human data ratios that prevent recursive model collapse.
- Quantitative threshold indicators for synthetic data pollution in training pipelines.
References & Prior Work
- The Curse of Recursion: Training on Generated Data Makes Models Forget — Shumailov et al., Nature (2024).
- Synthetic Data Compounding and Alignment Stability — Sleepers Research Paper (2026).
Interested in this research direction?
Sleepers Research welcomes inquiries from teams exploring autonomous agent security and applied machine learning.
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