arXiv cs.AIPaper
Program Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs
This challenges the assumption that reasoning should live in weights. By separating logic into an explicit program with intermediate verification, PLVR makes credit assignment transparent and the learned behavior portable. The mechanism (symbolic backprop via type inference) is elegant. For builders: this is a design pattern for agent systems where you want auditability and don't want to fine-tune. For researchers: this is a different post-training paradigm worth exploring.