A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond (2503.21614v1)

Abstract: Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively long reasoning traces, which are often filled with redundant content (e.g., repeated definitions), over-analysis of simple problems, and superficial exploration of multiple reasoning paths for harder tasks. This inefficiency introduces significant challenges for training, inference, and real-world deployment (e.g., in agent-based systems), where token economy is critical. In this survey, we provide a comprehensive overview of recent efforts aimed at improving reasoning efficiency in LRMs, with a particular focus on the unique challenges that arise in this new paradigm. We identify common patterns of inefficiency, examine methods proposed across the LRM lifecycle, i.e., from pretraining to inference, and discuss promising future directions for research. To support ongoing development, we also maintain a real-time GitHub repository tracking recent progress in the field. We hope this survey serves as a foundation for further exploration and inspires innovation in this rapidly evolving area.

Overview of "A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond"

The paper "A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond" presents a thorough examination of the state-of-the-art techniques and methodologies aimed at enhancing the efficiency of large reasoning models (LRMs). This work focuses particularly on the inefficiencies that arise due to the verbose nature of current reasoning processes, which often involve lengthy and redundant reasoning traces. It provides a detailed analysis of how these inefficiencies manifest during various phases such as pretraining, supervised fine-tuning (SFT), reinforcement learning (RL), and inference, and discusses potential solutions that can address these issues effectively.

Common Inefficiencies in Large Reasoning Models

The paper begins by identifying common patterns of inefficiency in LRMs, which are distinguished from traditional LLMs by their capability to perform chain-of-thought (CoT) reasoning. The authors highlight that while CoT reasoning enhances the problem-solving ability of LRMs, it also leads to increased computational complexity and latency due to the generation of excessive reasoning tokens. This inefficiency is particularly pronounced in scenarios where simple tasks are overanalyzed, leading to unnecessary computational overhead.

Approaches to Enhancing Efficiency

The survey categorizes the approaches to improving LRM reasoning efficiency into several key areas:

1. Inference Optimization: Techniques such as length budgeting help manage token usage by allotting a set budget for reasoning steps. System-switch strategies dynamically toggle between fast, intuitive, and slow, deliberative reasoning modes, while model-switch methods allocate resources across different models optimized for varying complexities of tasks. Additionally, parallel search tactics attempt to reduce inference latency by concurrently processing multiple reasoning paths.
2. Supervised Fine-Tuning (SFT): The paper discusses reasoning chain compression and latent-space SFT as methods to internalize efficient reasoning during the training phase. These approaches focus on minimizing token redundancy and utilizing concise latent representations for reasoning.
3. Reinforcement Learning (RL): The authors extend their discussion to RL-based models, exploring how efficiency can be improved by integrating length penalties directly into the reward functions and how alternative methods might prioritize concise reasoning paths without explicitly relying on length rewards.
4. Pretraining with Subquadratic Attention: Improving the efficiency of LLMs from a structural perspective, the paper presents models that employ subquadratic complexity mechanisms, such as linear sequence modeling and sparse attention, to reduce the high computational demands typically associated with CoT reasoning.

Implications and Future Directions

The survey provides not only a comprehensive overview of current methodologies but also speculates on future developments in AI concerning efficient reasoning. The implications for both practical applications and theoretical advancements are significant. Efficient reasoning frameworks can substantially reduce the computational footprint of models, making them more suitable for real-world applications where resource constraints are a critical factor.

Moreover, the paper points towards exciting future directions, including efficient multimodal and video reasoning, which aim to transcend current limitations in complex, real-time reasoning scenarios. Future work may also explore adaptive reasoning strategies that seamlessly switch between depth and breadth, depending on context and computational availability.

Conclusion

In conclusion, this survey significantly contributes to understanding and addressing the inefficiencies inherent in the reasoning processes of large reasoning models. By categorizing and evaluating a variety of approaches across the model lifecycle—from inference and SFT to RL and pretraining—it lays the groundwork for future innovations that strive to optimize reasoning efficiency without compromising performance. As AI systems continue to evolve, the insights from this paper will likely guide the development of more refined models that balance computational demand with cognitive complexity, enhancing their applicability across diverse domains.