ALBERT (A Lite BERT) reduces BERT parameters through factorization and sharing while maintaining performance. Key techniques: Factorized embeddings: Decompose large embedding matrix into two smaller matrices. E = V x 128, then 128 x H, instead of V x H directly. Cross-layer sharing: Share parameters across all transformer layers. Same weights reused. Inter-sentence coherence: Replace NSP with harder sentence ordering prediction task. Parameter reduction: ALBERT-xxlarge has 12x fewer parameters than BERT-large but more layers. Trade-off: Fewer parameters but similar or slower inference (same compute, weights reused). Why it works: Embeddings are over-parameterized, and layers learn similar functions. Sharing acts as regularization. Variants: ALBERT-base, large, xlarge, xxlarge. xxlarge has only 223M params but 12 layers shared. Results: Competitive with BERT-large using fraction of parameters. State-of-art at time on some benchmarks. Use cases: When parameter count matters (mobile, edge) more than inference speed.