Curricula and Multi-Agent Drafting for Reinforcement-Learned Software Reasoning

Abstract

Reinforcement Learning For Software Reasoning has become a test of how well researchers can connect performance with evidence quality, resource limits, and transfer across settings. This comparative analysis considers organizing intermediate drafts and code lineage into learnable reasoning trajectories. The comparison integrates 2 focal papers with 13 independently retrieved publications verified through persistent DOI or publisher records. The analysis is organized around draft coordination, curriculum design, lineage graphs, reward hacking, and generalization. Instead of assuming that metrics from unlike protocols as commensurate, the review compares research framing, method assumptions, and test envelope. Across the literature, a robust inference is that advances in reinforcement learning for software reasoning become credible when representation, objective, and evaluation protocol are evaluated together and when uncertainty about distribution shift is reported explicitly. The comparative structure connects method selection to decision consequence and recurring validity threats, and proposes a research agenda centered on comparable baselines, sensitivity analysis, and retained provenance.