The State of LLM Reasoning Model Inference

The State of LLM Reasoning Model Inference Inference-Time Compute Scaling Methods to Improve Reasoning Models Improving the reasoning abilities of large language models (LLMs) has become one of the hottest topics in 2025, and for good reason. Stronger reasoning skills allow LLMs to tackle more complex problems, making them more capable across a wide range of tasks users care about. In the last few weeks, researchers have shared a large number of new strategies to improve reasoning, including scaling inference-time compute, reinforcement learning, supervised fine-tuning, and distillation. And many approaches combine these techniques for greater effect. This article explores recent research advancements in reasoning-optimized LLMs, with a particular focus on inference-time compute scaling that have emerged since the release of DeepSeek R1. Implementing and improving reasoning in LLMs: The four main categories Since most readers are likely already familiar with LLM reasoning models, I will keep the definition short: An LLM-based reasoning model is an LLM designed to solve multi-step problems by generating intermediate steps or structured "thought" processes. Unlike simple question-answering LLMs that just share the final answer, reasoning models either explicitly display their thought process or handle it internally, which helps them to perform better at complex tasks such as puzzles, coding challenges, and mathematical problems. In general, there are two main strategies to improve reasoning: (1) increasing training compute or (2) increasing inference compute, also known as inference-time scaling or test-time scaling. (Inference compute refers to the processing power required to generate model outputs in response to a user query after training.) Note that the plots shown above make it look like we improve reasoning either via train-time compute OR test-time compute. However, LLMs are usually designed to improve reasoning by combining heavy train-time compute (extensive training or fine-tuning, often with reinforcement learning or specialized data) and increased test-time compute (allowing the model to "think longer" or perform extra computation during inference). To understand how reasoning models are being developed and improved, I think it remains useful to look at the different techniques separately. In my previous article, Understanding Reasoning LLMs, I discussed a finer categorization into four categories, as summarized in the figure below. Methods 2-4 in the figure above typically produce models that generate longer responses because they include intermediate steps and explanations in their outputs. Since inference costs scale with response length (e.g., a response twice as long requires twice the compute), these training approaches are inherently linked to inference scaling. However, in this section on inference-time compute scaling, I focus specifically on techniques that explicitly regulate the number of generated tokens, whether through additional sampling strategies, self-correction mechanisms, or other methods. In this article, I focus on the interesting new research papers and model releases focused on scaling inference-time compute scaling that followed after the DeepSeek R1 release on January 22nd, 2025. (Originally, I wanted to cover methods from all categories in this article, but due to the excessive length, I decided to release a separate article focused on train-time compute methods in…