Efficient Reasoning in Large Language Models

The field of large language models is moving towards more efficient and effective reasoning capabilities. Recent research has focused on leveraging neuro-symbolic coordination, test-time fine-tuning, and self-guided efficiency enhancement to improve performance on complex tasks. Notably, the use of reinforcement learning and large-scale training has demonstrated state-of-the-art results, but also raises concerns about computational costs and privacy leakage.

Overall, the field is advancing towards more robust and general-purpose reasoning systems, with a growing emphasis on evaluating test-time learning capabilities and addressing the intellectual gap between models and humans.

Some particularly noteworthy papers include:

  • Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models, which introduces an improved version of the Draft, Sketch, and Prove framework, achieving comparable performance without large-scale training.
  • Don't throw the baby out with the bathwater: How and why deep learning for ARC, which demonstrates state-of-the-art performance on the Abstraction and Reasoning Corpus using deep learning and on-the-fly neural network training.
  • Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement, which proposes lightweight methods to enhance efficiency in large reasoning models, reducing reasoning length while preserving task performance.

Sources

Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models

Don't throw the baby out with the bathwater: How and why deep learning for ARC

Excessive Reasoning Attack on Reasoning LLMs

How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison

LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

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