Instruction tuning narrows model outputs, study shows

Researchers have identified significant trade-offs in instruction tuning, a common refinement technique for language models. The method enhances instruction-following but simultaneously narrows output diversity and reduces contextual adaptability. In response, a research team created Spectrum Suite and developed Spectrum Tuning as an alternative post-training approach. The new method aims to maintain instruction compliance while preserving broader output diversity and model flexibility.