Sub-1-Bit LLM Compression via Latent Factorization

LittleBit and LittleBit-2 are NeurIPS 2025 and ICML 2026 methods for compressing large language models to sub-1-bit regimes through latent factorization, binarization, and learned scales. LittleBit-2 improves initialization via Joint-ITQ alignment without adding inference overhead.