Multi-Objective vs. Single-Objective Reinforcement Learning in Code Generation Benchmarks

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the performance of Multi-Objective Reinforcement Learning (MORL) for preference alignment compare to single-objective methods in terms of HumanEval-JavaScript and HumanEval-Java pass@k. 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. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the performance of Multi-Objective Reinforcement Learning (MORL) for preference alignment compare to single-objective methods in terms of HumanEval-JavaScript and HumanEval-Java pass@k scores when evaluated across diverse user preference distributions?

Autonomous literature synthesis. Automated review score: 8.6/10. Full text and citation available at Assignee Research.