From Theory to Practice: Compute-Optimal Inference Strategies for Language Model

Large language models (LLMs) have demonstrated remarkable performance across multiple domains, driven by scaling laws highlighting the relationship between model size, training computation, and performance. Despite significant advancements in model scaling, a critical gap exists in comprehending how computational resources during inference impact model performance post-training. The complexity arises from balancing performance improvements against the […]

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