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Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evolving recurrent states poses new challenges for low-bit deployment. Existing post-training quantization (PTQ) methods, largely designed for feed-forward Transformers, f...
This work introduces MetaEncoder, which fine-tunes a pre-trained Muse-Glimmer 30B decoder into an instruction-following decision-making encoder, and employs a bi-encoder architecture trained via unidirectional contrastive learning for request-candidate alignment.
All for 1-Bit (AF1) is proposed, a genuine 1-bit PTQ framework for LLMs that consistently outperforms existing binarization-based PTQ methods in perplexity and zero-shot accuracy, providing a practical path toward deployable genuine 1-bit compression for LLMs.
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