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RATIO: Reasoning Analysis and Token-level Inference Optimization for Quantized Reasoning Models
Post-training quantization (PTQ) has become a widely adopted technique for reducing the memory footprint and inference cost of large language models (LLMs). However, recent studies reveal that when applied to reasoning models, PTQ not only degrades reasoning performance but also exacerbates overthin
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