Safety Training
The process of making LLMs safer through specialized training – includes RLHF, DPO, Constitutional AI, and red-teaming-based training.
Safety Training makes LLMs safe through RLHF, DPO, and red teaming – transforms a raw language model into a responsible product. The core behind ChatGPT and Claude.
Explanation
Safety training has multiple stages: SFT on safe responses, RLHF/DPO for preference alignment, red teaming for vulnerability discovery, iterative retraining.
Marketing Relevance
Safety training determines whether an LLM is production-ready. Without it, models generate toxic, false, or dangerous outputs.
Common Pitfalls
Over-safety makes models useless (refuse to answer harmless queries). Safety can be bypassed by jailbreaks. Bias in safety data.
Origin & History
OpenAI introduced systematic safety training with InstructGPT (2022). Anthropic extended it with Constitutional AI. Meta released Llama 2 with a detailed safety training paper. Safety training is now standard for all commercial LLMs.
Comparisons & Differences
Safety Training vs. RLHF
RLHF is a specific safety training method; Safety Training encompasses the entire process including SFT, red teaming, etc.
Safety Training vs. Guardrails
Safety training changes the model itself; Guardrails are external filters that check unmodified outputs afterwards.