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Spurious correlations in foundation models can be effectively disentangled using a dual-branch approach, achieving superior bias mitigation with minimal parameter adjustments.
Off-policy degree in RLVR updates can drastically change which tokens drive learning, leading to a new adaptive method that outperforms traditional baselines.
