Quantify PRISM’s gains at large (70B+) model scales

Determine the magnitude of performance improvements achieved by PRISM (Persona Routing via Intent-based Self-Modeling), a gated LoRA distillation approach for intent-conditioned persona routing, when applied to large-scale language models with 70B or more parameters.

Background

The study evaluates PRISM on 7–8B parameter instruction-tuned and reasoning-distilled LLMs, finding that persona prompts consistently improve alignment-dependent tasks while harming pretraining-dependent knowledge retrieval, and that PRISM’s gated distillation preserves knowledge accuracy while improving alignment metrics.

However, the authors explicitly note that they have not tested PRISM at larger scales (e.g., 70B+), leaving open the question of how the magnitude of PRISM’s improvements changes with model size.

References

Model scale. Our experiments are limited to 7--8B parameter models. While the findings on persona sensitivity and task-type dependence are likely to generalize, the magnitude of PRISM's improvements at larger scales (e.g., 70B+) remains untested.

— Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM  (2603.18507 - Hu et al., 19 Mar 2026) in Limitations, Model scale paragraph