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Generalization in LLMs hinges on training reward saturation dynamics, with reasoning faithfulness emerging as a critical predictor of success under weak supervision.
Forget brute-force scaling: targeted data curation for RLVR can unlock surprisingly large gains in LLM reasoning.
