AI21 Labs reduces LLM training waste with model-agnostic padding minimization

Jul 11, 2026

AI21 Labs has developed a model-agnostic method that eliminates approximately 90% of padding-related overhead in large language model (LLM) training. Their approach uses micro-batch-level truncation and padding-aware micro-batching to address inefficiencies, particularly in hybrid Transformer-SSM models where sequence packing is not easily applicable.

Why it matters: This technique can significantly reduce compute waste and training costs for large language models without requiring model-specific modifications.

Full story at: AI21 Labs ↗