Knowledge Distillation and Dynamic Learning Rates for Stable Code Generation Models - Assignee Research (Assignee Research, 2026)
This report synthesises findings from 15 peer-reviewed papers addressing the following research question: Does integrating knowledge distillation with dynamic learning rate schedules improve the stability of code generation models when evaluated on out-of-distribution LiveCodeBench problems. 4 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.3/10. This report is a machine-g
Machine-generated literature synthesis.