Self-Supervised ARS Models Outperform Supervised Baselines in HumanEval Scalability

This report synthesises findings from 7 peer-reviewed papers addressing the following research question: How does the scalability of ARS models trained with self-supervised learning compare to supervised baselines in terms of pass@1 scores on HumanEval when increasing the number of support examples from. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the scalability of ARS models trained with self-supervised learning compare to supervised baselines in terms of pass@1 scores on HumanEval when increasing the number of support examples from 1 to 5?

Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.