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.
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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:
Gravity Wells
Gravity wells describe economic dynamics where scarce resources flow disproportionately to entities with the greatest ability to pay and deploy, creating...
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...
Multimodal AI
Multimodal AI refers to models that process and generate multiple data types—text, images, audio, and video—within a single system.