Reducing LLM training waste with model-agnostic padding minimization

Efficient training of large language models (LLMs), particularly in online reinforcement learning (RL) environments, faces significant challenges due to padding-related inefficiencies. Padding, used to standardize sequence lengths for model processing, can cause up to 50% of computational resources to be wasted, especially in hybrid models like Transformer-SSM, where traditional sequence packing methods are not applicable. By employing a model-agnostic approach that involves micro-batch-level truncation and padding-aware dynamic micro-batching, researchers have successfully reduced padding overhead by approximately 90%. This strategy involves reorganizing sequences within micro-batches to minimize padding, significantly enhancing training efficiency across various model architectures without the need for architecture-specific modifications. The results show a dramatic reduction in policy update step times for models like Qwen2.5-7B and Jamba2-3B, achieving close to the efficiency of sequence packing without compromising model performance. This approach underscores the value of architecture-agnostic solutions in optimizing training systems initially designed for transformers, facilitating the adoption of new model architectures by reducing the need for specialized engineering solutions.