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Enhance Reasoning By Learning From Mistakes
This document presents an in-depth exploration of the Mistake-Aware Peer-Review Distillation (MAPD) methodology, a novel approach designed to enhance the reasoning capabilities of smaller language models (LMs) through innovative training techniques. By integrating feedback mechanisms that allow models to learn from their mistakes, MAPD offers a significant advancement in knowledge distillation.
Tag: KNOWLEDGE_DISTILLATION
AI technique transferring expertise from large to small models. Explore model compression, efficiency, and preserving performance in compact AI.