Author

Neehal Tumma

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Preprint Nov 2026

Quantizing Recurrent Language Models Is Easier Than You Think

Linear recurrences are becoming an increasingly common building block in large language models, replacing the growing KV cache of softmax attention with a fixed-size recurrent state that enables efficient decoding. However, the quantization stack has largely been developed around softmax Transformers, whose numerical p...

Philipp Nazari, Neehal Tumma, Daniela Rus et al. · 0 citations