The field of large language models (LLMs) is moving towards more efficient and effective reasoning capabilities. Recent developments have focused on improving the balance between performance and computational costs, with techniques such as inference-time scaling, reinforcement learning, and distillation being applied to enhance reasoning capabilities. Notable advancements include the development of methods to reduce token usage and improve inference efficiency, such as habitual reasoning distillation and model collaboration. Additionally, surveys and analyses have highlighted the importance of understanding the causes of reasoning inefficiency and the need for more concise reasoning chains. Overall, the field is shifting towards more efficient and effective reasoning mechanisms, with a focus on balancing performance and computational costs. Noteworthy papers include Reasoning Beyond Limits, which provides a comprehensive analysis of top LLM models and training methodologies, and TwT, which proposes a method for reducing inference-time costs through habitual reasoning distillation. Other notable papers include FReM, Efficient Inference for Large Reasoning Models, and Hawkeye, which propose novel methods for improving reasoning efficiency and effectiveness.
Advances in Efficient Reasoning for Large Language Models
Sources
Reasoning Beyond Limits: Advances and Open Problems for LLMs
FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering
Efficient Inference for Large Reasoning Models: A Survey
TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers' Guidance
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
Hawkeye:Efficient Reasoning with Model Collaboration
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
OpenThaiGPT 1.6 and R1: Thai-Centric Open Source and Reasoning Large Language Models
Style over Substance: Distilled Language Models Reason Via Stylistic Replication
Critical Thinking: Which Kinds of Complexity Govern Optimal Reasoning Length?
Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models
When Reasoning Meets Compression: Benchmarking Compressed Large Reasoning Models on Complex Reasoning Tasks
A Survey of Scaling in Large Language Model Reasoning