Panoramic Analysis of Efficient Inference Technologies for Large Reasoning Models: From Explicit CoT Compression to Implicit Latent Reasoning

This article provides an in-depth analysis of the latest advances in efficient inference technologies for Large Reasoning Models (LRMs), covering two core technical routes: explicit compact Chain of Thought (CoT) and implicit latent Chain of Thought, while also discussing the challenges and future development directions in this field. Original author/maintainer: yueliu1999; Source: GitHub repository Awesome-Efficient-Inference-for-LRMs (link: https://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs); Publication time: 2026-05-26T03:10:43Z.

With the emergence of LRMs such as OpenAI o1/o3, DeepSeek-R1, and Kimi k1.5, AI has made breakthroughs in solving complex tasks. However, explicit Chain of Thought (CoT) reasoning brings efficiency bottlenecks: surging token consumption, inflated memory usage, and prolonged inference time. In practical deployment, complex problems may require thousands to tens of thousands of tokens for reasoning, increasing costs and reducing real-time performance. Therefore, improving efficiency while maintaining reasoning quality has become a core issue.

To address the inference efficiency issue of LRMs, mainstream methods are divided into two categories:

Explicit Compact Chain of Thought (Explicit Compact CoT)

Retain the explicit reasoning structure and reduce tokens through compression, pruning, or reconstruction:

1. Inference chain compression: Remove redundant steps and retain key nodes;
2. Structured output optimization: Use symbolic/hierarchical structures to reduce tokens;
3. Dynamic inference depth adjustment: Adaptively adjust inference depth based on problem complexity.

Implicit Latent Chain of Thought (Implicit Latent CoT)

Encode reasoning in hidden states without generating explicit tokens:

1. Latent space reasoning: Perform multi-step reasoning in the internal latent space and output answers directly;
2. Hybrid reasoning architecture: Use explicit reasoning at key decision points to ensure interpretability, and implicit steps in between to improve efficiency;
3. Inference distillation and model merging: Distill the capabilities of large models into small models, or merge specialized models to reduce overhead.

Empirical evaluations of existing methods reveal:

1. Scenario differences: Explicit compact CoT better preserves accuracy in mathematical reasoning; implicit latent CoT has lower costs and similar effects in common sense/open-domain question answering;
2. Objective function challenges: Need to balance accuracy, token efficiency, latency, and memory, with different priorities for different scenarios (real-time interaction vs. batch processing);
3. Pareto frontier: Existing technologies can achieve Pareto improvements in performance and efficiency, but excessive compression leads to non-linear performance degradation, resulting in an "efficiency wall".

Efficient inference for LRMs still faces challenges:

1. Human-controllable reasoning: Users find it difficult to intervene in the reasoning process, requiring controllability;
2. Interpretability-efficiency trade-off: Implicit methods are efficient but sacrifice interpretability, and high-risk scenarios need to balance both;
3. Security assurance: Some compression methods are prone to adversarial attacks or hallucinations, requiring robustness;
4. Scenario expansion: Current research focuses on mathematics/code domains, needing to expand to multi-modal, long-document, cross-language reasoning, etc.

Future directions worth attention:

1. Model merging: Merge multi-task optimized models to reduce switching and loading overhead;
2. New architecture exploration: Go beyond Transformers, combining neuro-symbolic reasoning or external memory mechanisms;
3. Intelligent routing systems: Automatically select the optimal reasoning strategy based on problem characteristics;
4. Hardware-algorithm co-optimization: Design matching algorithms with dedicated hardware (TPU/ASIC) to improve system efficiency.

The efficiency of large reasoning models is key to AI moving from the laboratory to large-scale applications. The two technical routes (explicit and implicit) each have their advantages and disadvantages. Future breakthroughs may come from their fusion or new architectures. Researchers and engineers need to understand technical principles and trade-offs to choose solutions suitable for their scenarios. It is expected that efficient and powerful reasoning capabilities will become a standard configuration for AI systems.