Instructiontuned

Instruction tuning refers to the process of fine-tuning a pre-trained language model on a curated dataset of instruction–response pairs with the aim of improving the model’s ability to follow user instructions. The resulting models are often described as instruction-tuned. This approach is a form of supervised fine-tuning that emphasizes alignment with human intent rather than purely optimizing next-token prediction.

Datasets used for instruction tuning are diverse and task-spanning, covering areas such as summarization, translation, reasoning, coding, and question answering. Prompts illustrate preferred formats, constraints, and styles. Data can be collected from human-written demonstrations or generated with prompt templates, and sometimes synthetic data is produced by prompting a base model. The goal is to expose the model to a wide variety of instructions so it generalizes better to unseen prompts.

Instruction tuning is related to, but distinct from, reinforcement learning with human feedback (RLHF). In practice, instruction tuning often serves as a precursor or complement to RLHF, providing strong instruction-following behavior that can be further refined to align outputs with human preferences. It is also contrasted with task-specific fine-tuning, since instruction-tuned models aim to perform well across many tasks without dedicated fine-tuning for each one.

Limitations include dependence on the quality and breadth of the instruction dataset, potential amplification of biases present in the data, and the risk of overfitting to common instruction formats. The approach does not guarantee safe or correct outputs and may still generate harmful or erroneous responses under some prompts.

The term appears in research on open-domain instruction-following models and is associated with models capable of fluent, instruction-compliant outputs. Notable milestones include early instruction-tuned variants built on large language models and subsequent alignment efforts that incorporate human feedback.