Robustness of RLHF and Learned Q-Shaping in LLM Python Code Generation

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the comparative robustness of standard RLHF versus learned Q-shaping in maintaining pass@1 accuracy for LLMs when evaluating out-of-distribution Python code generation tasks from HumanEval. As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the comparative robustness of standard RLHF versus learned Q-shaping in maintaining pass@1 accuracy for LLMs when evaluating out-of-distribution Python code generation tasks from HumanEval?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.