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TACO effectively mitigates the reinforcement of erroneous reasoning in LLMs by distinguishing between useful and unreliable tokens, leading to improved training stability and performance.
On-policy distillation can lead to catastrophic length inflation in student models, but a simple fix stabilizes training and boosts performance by 7%.
