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AutoBackSwap effectively eliminates reliance on spurious backgrounds, achieving superior classification performance without needing counterexamples in the training set.
Reward redistribution can significantly enhance reasoning model performance by focusing on critical segments of Chain-of-Thought traces without incurring extra computational costs.
