parameterefficient

Parameterefficient, or parameter efficiency, is a term used to describe methods and properties that achieve competitive model performance while using a relatively small number of trainable parameters or reduced computational resources. It contrasts with full fine-tuning, where all parameters are updated for each new task or domain.

Common approaches include inserting small trainable adapter modules into frozen pre-trained networks, applying low-rank adaptation (LoRA), and using prompt-tuning or prefix-tuning. Other techniques involve weight sharing, model distillation, pruning, and quantization. Together, these strategies aim to adapt large models to new tasks with minimal training overhead and faster deployment.

Parameterefficient techniques are especially prominent in natural language processing with large transformer models, enabling rapid adaptation to new tasks, multi-task learning, or deployment on devices with limited memory or compute. They also support experimentation across domains such as vision and speech, where updating every parameter may be impractical.

Evaluation and trade-offs revolve around the number of trainable parameters, memory usage, and latency, often weighed against task accuracy. Typical challenges include potential modest losses in peak performance, the need for careful integration with existing training pipelines, and compatibility considerations across model architectures and hardware.

The concept gained prominence as model sizes grew in the late 2010s and early 2020s. Techniques such as adapters and LoRA are among the most cited approaches, reflecting a broader shift toward achieving strong performance with reduced parameter counts and greater flexibility in model deployment.