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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.
Nexus Sampling retains crucial tokens during KV cache eviction, achieving near-dense attention performance with dramatically reduced memory usage.
