RLHF
Reinforcement Learning from Human Feedback (RLHF) is the training technique that transformed large language models from impressive autocomplete engines into useful assistants by systematically aligning their outputs with human preferences. First popularized by OpenAI's InstructGPT paper in 2022, the process trains a reward model on thousands of human comparisons—which response is better?—then uses reinforcement learning to tune the base model toward responses humans actually prefer. This alignment layer is why modern AI can follow complex instructions, refuse harmful requests, and match organizational tone—making it the invisible substrate beneath every enterprise AI deployment.
Referenced in these posts:
Satisficing for LLMs
By applying Herbert Simon’s concept of satisficing to AI, this post argues that language models might prefer logical‐sounding content over emotional appeals,...
Related terms:
LLM (Large Language Model)
A large language model is a neural network with billions of parameters trained on massive text corpora to predict the next word in a sequence, powering tasks...
Temperature
Temperature is a parameter controlling a language model’s randomness: at 0 it always picks the most probable next token for deterministic, reliable output,...
Embeddings
Embeddings are numerical representations of text—vectors of hundreds or thousands of floating-point numbers—that capture semantic meaning in a form machines...