Efficient Reasoning in Large Language Models

The field of large language models is moving towards improving the efficiency of reasoning capabilities. Recent research has focused on developing methods to reduce the verbosity and redundancy of outputs, while maintaining or improving accuracy. One key direction is the use of parallelization and dynamic length rewards to encourage more efficient reasoning. Another area of research is the development of frameworks that can automatically identify and exploit opportunities for parallelization during the reasoning process.

Notable papers in this area include SPRINT, which enables interleaved planning and parallelized execution in reasoning models, and Token Signature, which predicts chain-of-thought gains with token decoding features. Other papers, such as Bingo and ReCUT, have proposed reinforcement learning frameworks to boost efficient reasoning and balance reasoning length and accuracy.

Overall, the field is moving towards more efficient and effective reasoning capabilities in large language models, with a focus on reducing computational latency and improving overall performance.

Sources

SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models

Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models

Unlocking Recursive Thinking of LLMs: Alignment via Refinement

Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and Significance-based Reinforcement Learning

Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency

On Reasoning Strength Planning in Large Reasoning Models

Brevity is the soul of sustainability: Characterizing LLM response lengths

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning

Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation

Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

Data Shifts Hurt CoT: A Theoretical Study

PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization