The State of LLM Reasoning Models

Recent research aims to enhance reasoning in large language models through inference-time compute scaling, allowing models to "think longer" during generation. Key developments include "wait" tokens, self-backtracking, and dynamic compute allocation. Studies show that effective inference scaling can enable smaller models to outperform larger ones on complex tasks. However, optimal techniques vary by task, and increased compute time may affect response speed. LLM providers are increasingly adding optional "thinking" modes to their models.