Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs
Self-Consistency (SC) is a decoding strategy that samples diverse reasoning paths and selects the most consistent answer, demonstrating strong performance on complex reasoning problems. However, the excessive token consumption incurred by generating multiple reasoning paths has been identified as a major limitation of SC. To improve computational efficiency, several studies have proposed strategies that adjust the number of reasoning paths or allocate resources differentially according to problem difficulty. Nevertheless, most existing methods categorize difficulty into a few fixed levels, fai
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 54%Exploring self-distilled reasoning for supervised fine-tuning with Amazon Nova →
- PossiblePossibly related (embedding) · 50%Northwind AI →
- PossiblePossibly related (embedding) · 48%New benchmark exposes reasoning gaps in top models →
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- LinkedLinked via arxiv author · 85%Sihyeong Yeom →
“Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs”
- LinkedLinked via arxiv author · 85%Geon Park →
“Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs”
- LinkedLinked via arxiv author · 85%Geunyeong Jeong →
“Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs”
- LinkedLinked via arxiv author · 85%Taewoong Yoon →
“Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs”