Toward Credible Reinforcement Learning For Software Reasoning And Automated Program Repair: Data Quality, Calibration, and External Validity

Abstract

This review examines a shared methodological problem in reinforcement learning for software reasoning and automated program repair: how evidence from multi-agent chain-of-draft reasoning optimized with reinforcement learning can be placed in analytical dialogue with execution-grounded reinforcement learning with sequence- and line-level reward models without erasing differences in scale, assumptions, or intended use. The analysis combines two focal publications with 12 previously verified sources and organizes the evidence around draft coordination, curriculum design, lineage graphs, reward hacking, and generalization. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. The synthesis shows that draft coordination cannot be interpreted independently of curriculum design, while lineage graphs determines whether an apparent improvement remains meaningful outside the original setting. The strongest claims are therefore those that expose sensitivity, failure conditions, and residual uncertainty. The article concludes with a research agenda built around transparent comparators, targeted stress tests, and evidence records that can be reused without overstating causal or practical reach.