Evidence Alignment and Transfer Boundaries in Latent Policy Optimization And Automated Program Repair

Abstract

This review examines a shared methodological problem in latent policy optimization and automated program repair: how evidence from iterative information-bottleneck control of latent policy optimization 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 latent representations, mutual information, policy updates, reward alignment, and training stability. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. The synthesis shows that latent representations cannot be interpreted independently of mutual information, while policy updates 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. 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.