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Inference-Time Compute Scaling Methods to Improve Reasoning Models

sebastianraschka.com · 4,683 words · saved by 1 readers

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. Table of Contents 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”

The State of LLM Reasoning Model Inference Inference-Time Compute Scaling Methods to Improve Reasoning Models Sebastian Raschka, PhD Mar 08, 2025 428 11 32 Share 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 learn

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