This research presents comprehensive approaches to optimize inference efficiency in Large Reasoning Models (LRMs) while preserving their reasoning capabilities.

• Token Efficiency - Techniques to reduce the verbose reasoning process that leads to high token usage
• Memory Optimization - Methods to decrease memory consumption during complex reasoning tasks
• Inference Acceleration - Strategies to speed up the deliberative reasoning process
• Performance Preservation - Approaches that maintain reasoning quality while improving computational efficiency

For engineering teams, these optimization techniques offer practical solutions to deploy sophisticated reasoning models in production environments with real-world constraints on computational resources and response time requirements.

Efficient Inference for Large Reasoning Models: A Survey