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explainers

Articles

  • Process Reward Models: How Step-by-Step Supervision and Search Drive LLM Reasoning

    When large language models tackle complex multi-step reasoning (such as formal mathematics, algorithm synthesis, or multi-hop logic), evaluating only the final answer creates a severe credit assignment bottleneck. An outcome-based verifier can confirm whether a final numerical result is correct, but it cannot determine whether the underlying derivation was logically sound or reached the right answer through compounding hallucinations and lucky cancellations. Process Reward Models (PRMs) resolve

    1 min

  • What Is a KV Cache? The Memory Bottleneck Behind LLM Serving

    What Is a KV Cache? The Memory Bottleneck Behind LLM Serving Every token a large language model generates costs more than compute. It costs memory. The mechanism that stores the model's working state across a generation is called the KV cache, and managing it well is what separates a serving system that feels fast from one that stalls. This explainer covers what the KV cache is, why it wastes memory, and how the PagedAttention algorithm from Berkeley's vLLM project reshaped how it is handled.

    1 min