Fast and Accurate Probing of In-Training LLMs' Downstream Performances

ArXi:2604.01025v1 Announce Type: cross The paradigm of scaling Large Language Models (LLMs) in both parameter size and test time has pushed the boundaries of AI capabilities, but at the cost of making the traditional generative evaluation paradigm prohibitively expensive,. therefore. making the latency of LLM's in-