Evidence Alignment and Transfer Boundaries in Automated Program Repair And Reinforcement Learning For Software Reasoning

Abstract

Two distinct lines of inquiry—execution-grounded reinforcement learning with sequence- and line-level reward models and multi-agent chain-of-draft reasoning optimized with reinforcement learning—converge on a practical question for automated program repair and reinforcement learning for software reasoning: what evidence is needed before a reported advantage becomes a defensible basis for explanation, comparison, or deployment? The review draws on two focal records and 12 established sources already present in the project evidence cache. Its comparative framework links execution signals, credit assignment, and patch validity to downstream questions of cross-language transfer and benchmark design. Across the evidence base, the decisive issue is alignment: execution signals shapes what is observed, credit assignment shapes how it is compared, and benchmark design governs whether the conclusion can be transferred. Uncertainty is most informative when reported as part of the result rather than treated as a postscript. The resulting framework supports reproducible comparison while preserving differences between study designs, and it identifies concrete points at which transfer claims should be narrowed or retested.