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Adapting supervision weights based on the evolution of divergence histories boosts reasoning performance in language models without extra computational overhead.
Scaling zero RL to a trillion parameters reveals that models can spontaneously develop advanced cognitive behaviors, making traditional heuristics obsolete.
