Continual Learning Strategies and Code Generation Retention in Large Language Models

This report synthesises findings from 16 peer-reviewed papers addressing the following research question: What is the impact of continual learning strategies on the retention of code generation capabilities in large language models as measured by performance degradation on MultiPL-E after sequential task. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the impact of continual learning strategies on the retention of code generation capabilities in large language models as measured by performance degradation on MultiPL-E after sequential task training?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.