Rohan Paul posted on X about a recent Amazon paper examining KV-cache policies in large language models. The post explains that these policies, typically viewed as tools for managing inference efficiency, also influence the training process. According to the tweet, aligning fine-tuning methods with the specific KV-cache approach used later can help models avoid failures in handling long contexts. Paul notes that when an LLM is expected to discard portions of its context during inference, training should incorporate similar forgetting mechanisms. The message includes a generated headline referencing the paper's focus on matching KV-cache policy during fine-tuning.