Comparative Evidence for Reinforcement Learning For Software Reasoning And Automated Program Repair: Comparative Methods and Boundary Conditions

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 review draws on two focal records and 12 established sources already present in the project evidence cache. Its comparative framework links draft coordination, curriculum design, and lineage graphs to downstream questions of reward hacking and generalization. Comparison reveals recurring trade-offs among draft coordination, curriculum design, and lineage graphs. 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.