Q-Shaping vs. Reward Shaping for Code Generation Accuracy Under Cross-Lingual Shifts

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does Q-shaping compare to reward shaping in maintaining code generation accuracy on DS-1000 under cross-lingual distribution shifts. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 10 claims were extracted from source literature; 9 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: How does Q-shaping compare to reward shaping in maintaining code generation accuracy on DS-1000 under cross-lingual distribution shifts?

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