Instruction Tuning

Instruction tuning is the post-pretraining adaptation technique that teaches a base LLM to follow natural-language instructions by training it on datasets of (instruction, response) pairs. The technique was popularized by Google's FLAN (2021), T0 (2022), and OpenAI's InstructGPT (2022), and is now the standard recipe for converting a raw language model into a useful assistant. Instruction-tuned models can generalize to novel instructions they were never explicitly trained on — a capability called "instruction following generalization" that is foundational to modern LLM usefulness. Common instruction datasets include FLAN-v2, Alpaca, Dolly-15k, OpenAssistant Conversations, ShareGPT, and the proprietary datasets used by frontier labs. Instruction tuning typically uses SFT as the training algorithm with LoRA or full fine-tuning. The diversity and quality of the instruction dataset matters more than raw size — the LIMA paper showed competitive results with just 1,000 examples. AI governance teams document instruction-tuning datasets in their AI compliance lineage because biases, errors, or harmful examples in the training data can persist in deployed model behavior.

Instruction-tuned model governance in Centralpoint: Centralpoint routes generation to instruction-tuned models from OpenAI, Anthropic, Google, Meta, and self-hosted alternatives — all in one model-agnostic platform with consistent token metering. Prompts stay local, supports both generative and embedded models, and deploys assistants through one line of JavaScript.


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