From Mechanism to Decision in Automated Program Repair And Reinforcement Learning For Software Reasoning: Cross-Scale Reasoning and Reproducibility

Abstract

The literature on automated program repair and reinforcement learning for software reasoning contains a recurring tension between methodological novelty and evidential comparability. By reading execution-grounded reinforcement learning with sequence- and line-level reward models alongside multi-agent chain-of-draft reasoning optimized with reinforcement learning, this article clarifies the conditions under which their conclusions can support a common research argument. The analysis combines two focal publications with 12 previously verified sources and organizes the evidence around execution signals, credit assignment, patch validity, cross-language transfer, and benchmark design. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. Comparison reveals recurring trade-offs among execution signals, credit assignment, and patch validity. These trade-offs do not support a universal ranking; instead, they identify the operating envelope within which each method remains credible and the perturbations most likely to expose fragile conclusions. 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.