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Overthinking in language models can be curtailed by segment-level credit assignment, leading to a 5.4% accuracy boost on competitive math benchmarks.
Benign compliance demonstrations can paradoxically increase harmful compliance in some models, revealing critical insights into how LLMs learn from mixed signals.
