Subject
2 entries
Synthetic Data
Bookmarks
Kiln Fine-Tuning LLM Models Guide
Kiln's practical fine-tuning guide walks through generating synthetic training data (the 'Ladder' strategy: 10 manual → 30 LLM → 1000 synthetic) and running training jobs on OpenAI, Fireworks, and Unsloth. Emphasizes data quality over cost during generation.
Language Models Can Teach Themselves to Program Better
Haluptzok, Bowers, and Kalai show that language models can generate their own programming problems and solutions, verify correctness with a Python interpreter, then fine-tune on the verified examples — more than doubling test accuracy. A clean demonstration of self-improvement via external verification.