CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

CritICL is an inference-time framework that enhances reasoning in large language models by using failure patterns from weaker models as critique-based in-context examples. It offers two variants: CritICL-dynamic, which predicts input-specific failure modes, and CritICL-static, which applies a global failure mode profile. Experiments show that CritICL outperforms standard in-context learning and rivals test-time scaling methods while using fewer generations and lower token costs.

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