RPRA: Predicting an LLM-Judge for Efficient but Performant Inference
Large language models (LLMs) face a fundamental trade-off between computational efficiency (e.g., number of parameters) and output quality, especially when deployed on computationally limited devices such as phones or laptops. One way to address this challenge is by following the example of humans and have models ask for help when they believe they are incapable of solving a problem on their own; we can overcome this trade-off by allowing smaller models to respond to queries when they believe they can provide good responses, and deferring to larger models when they do not believe they can. To this end, in this paper, we investigate the viability of Predict-Answer/Act (PA) and Reason-Predi...
| Authors | Dylan R. Ashley, Gaël Le Lan, Changsheng Zhao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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