Invariant Gradient Alignment for Robust Reasoning Distillation

Large language models (LLMs) suffer from shortcut learning: they systematically fail on out-of-distribution (OOD) inputs whose semantic surface differs from training data, even when the logical structure is identical. This undermines knowledge distillation pipelines that transfer chain-of-thought reasoning to smaller students. We introduce Invariant Gradient Alignment (IGA), a training framework that aligns gradient updates across semantically diverse but logically isomorphic examples via three

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ethics.ai (3 June 2026), “Invariant Gradient Alignment for Robust Reasoning Distillation,” evidence record 1489, https://ethics.ai/record/1489 (originally published by arXiv).

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