What is post-training data?
Post-training is everything after initial model training: fine-tuning, RLHF, evaluation and alignment. It depends on targeted, real-world data and is a fast-growing share of AI data spending.
Beyond the base model
A base model learns general language and knowledge. Post-training makes it useful: fine-tuning on domain examples, aligning it with human preferences, and evaluating it on real tasks.
Why it drives data demand
- Post-training needs specific, high-quality examples, not bulk web text.
- Each domain and task needs its own examples.
- Evaluation requires real task-and-outcome records.
Industry reporting documents growing spend on post-training data as labs compete on model quality rather than scale alone (Reuters).
What this means for partners
Post-training examples come from real work — exactly what companies with 50+ full-time employees at peak record daily. Introduce one to SourceX; if a deal closes and SourceX collects its fee, you earn 25% of that fee, up to $100,000 per company.
- Step 1Share your linkSend your personal link to a company you know.
- Step 2Company appliesThe company applies itself at /apply.
- Step 3Buyer selects and paysThe buyer selects and pays for the data and SourceX receives its fee.
- Step 4You get your rewardYour share of SourceX fees becomes payable.
Free resources
- Client data licensing eligibility checker — A transparent preliminary screen for one company.
- Enterprise value calculator — Enterprise value from equity value, debt and cash.
- Earnout scenario calculator — Probability-weighted earnout value and its present value.
- All free tools · MCP resource center
By SourceX Partnerships Team · Published 2026-10-09 · Updated 2026-10-09
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