The field of large language models is moving towards developing more efficient reasoning methods. Researchers are focusing on reducing the computational cost and improving the accuracy of these models. One of the key directions is the development of adaptive reasoning methods that can adjust the thinking length according to the problem difficulty. Another important area is the use of distillation techniques to transfer reasoning capabilities to smaller models, making them more efficient and scalable. Noteworthy papers in this regard include Adaptive Effort Control, which enables fine-grained control over the amount of thinking used for a particular query, and DeepCompress, which employs a dual-reward strategy to enhance both the accuracy and efficiency of large reasoning models. Other notable papers include DART, which proposes a difficulty-adaptive reasoning truncation framework, and BARD, which introduces a budget-aware reasoning distillation method. These advancements have the potential to significantly improve the efficiency and accuracy of large language models, making them more suitable for real-world applications.
Efficient Reasoning in Large Language Models
Sources
e1: Learning Adaptive Control of Reasoning Effort
Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement
DeepCompress: A Dual Reward Strategy for Dynamically Exploring and Compressing Reasoning Chains
Word Salad Chopper: Reasoning Models Waste A Ton Of Decoding Budget On Useless Repetitions, Self-Knowingly
DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals
Continual Learning, Not Training: Online Adaptation For Agents
DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models
Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series
BARD: budget-aware reasoning distillation
Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
Logit-Entropy Adaptive Stopping Heuristic for Efficient Chain-of-Thought Reasoning