Fine-Tuning as a Service (Vintage Data / SYNTH Methodology)
This entry captures the synthetic data methodology embedded in Fine-Tuning as a Service. See that page for the full platform evaluation.
SYNTH Methodology
- Fully synthetic generalist training environment — trains SOTA small models without organic data
- Used by Step-Fun (deep-research model) and multiple academic projects
- Moving to agentic use cases requires qualitative step up: synthetic generation must actively create and solve real problems (not just verbalize existing data)
Why Fine-Tune Over Prompt
- Better performance on smaller models via specialization
- Accuracy on formal rules (math, structured output, rule-based systems)
- Style customization without ad-hoc prompts
- Built-in I/O structuration (markdown-delimited rubrics)
- Effective token use at scale
The Agentic Data Gap
Early agentic traces had ~30% faulty tool calls (bad JSON). Getting reliable agentic behavior requires training on high-quality agentic traces, not just prompting general models.