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Premature verification and flawed assumptions in reasoning traces can lead to dramatic drops in LLM accuracy, but targeted interventions can recover performance by over 70%.
RL models not only outperform SFT counterparts in reasoning tasks but also develop a hierarchical architecture that enhances representational quality and processing efficiency.
