Evidence Alignment and Transfer Boundaries in Automated Program Repair And Reinforcement Learning For Software Reasoning: BoostAPR Boosting Automated Program and Evo-CuRL Curriculum-Aware Reinforcement Learning

Abstract

Progress in automated program repair and reinforcement learning for software reasoning depends on more than accumulating favorable results. This critical synthesis connects execution-grounded reinforcement learning with sequence- and line-level reward models with curriculum-aware reinforcement learning over code-lineage graphs and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. Two target papers are triangulated against 12 locally validated publications. The comparison follows execution signals, credit assignment, patch validity, cross-language transfer, benchmark design and deliberately separates mechanistic interpretation from performance ranking, because the latter can conceal incompatible experimental or operational conditions. 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. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.