Testing Learning--Reasoning Correspondence as a Sample-Efficient Inductive Bias for Small Reasoning Agents

Learning--Reasoning Correspondence (LRC) proposes that augmenting training examples with symbolic-style inference tuples can improve small-agent learning. We test this claim in a controlled finite replication that fixes rule-chain and symbolic gridworld task families, ten paired seeds, size-matched neural and neuro-symbolic conditions, and oracle trace-bearing tuple diagnostics. Relevant regularized LRC improves final out-of-distribution accuracy in both executed local benchmarks: 0.945 versus 0.496 for the neural baseline on rule chains and 0.905 versus 0.405 on symbolic gridworld planning. Mechanism-isolation margins remain positive against noisy-tuple and no-regularizer ablations, with bootstrap intervals [0.207, 0.219] and [0.236, 0.253]. Tuple confidence is also better calibrated and more faithful than noisy augmentation under the available oracle traces. The stronger sample-efficiency claim is not established: all predeclared samples-to-threshold comparisons at 0.80 and 0.90 validation accuracy are censored on the executed grid. The evidence therefore supports LRC as a local mechanism for final OOD generalization in controlled finite tasks, but weakens claims about threshold sample complexity and leaves public bAbI/MiniGrid replication as future work.