A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.
This survey systematically categorizes and reviews methods for efficient reasoning in LLMs, addressing the overthinking phenomenon by optimizing models, outputs, and input prompts.
Chia-Hsuan Lee, Sihui Dai, Mingyang Zhou, et al.
DASH assigns segment-level credit based on drift from correctness to reduce overthinking in reasoning language models, improving accuracy on competition math benchmarks.
Unknown
This paper proposes methods to reduce overthinking in large reasoning models, cutting computational costs while maintaining performance.