A naukri.com initiative
Towards Data Science
7h
287
Empowering LLMs to Think Deeper by Erasing Thoughts
- Recent large language models (LLMs) such as OpenAI’s o1/o3, DeepSeek’s R1, and Anthropic’s Claude 3.7 exhibit enhanced reasoning capabilities through deep thinking using chain-of-thought (CoT) approach.
- The CoT-based test-time scaling may hit ceilings due to exceeding model context windows, burying critical information, and high self-attention complexities.
- The article proposes a new reasoning paradigm named PENCIL that allows LLMs to both generate and erase thoughts for optimal reasoning efficiency.
- PENCIL uses erasure mechanism inspired by logic rewriting rules and functional programming to discard intermediate thoughts when not needed.
- PENCIL supports various reasoning patterns like task decomposition, branch and backtrack, and summarization/tail recursion for efficient problem-solving.
- PENCIL demonstrates significant space efficiency in tasks like Boolean Satisfiability (SAT) compared to traditional CoT, improving computational resource usage.
- Experimental results reveal that PENCIL outperforms CoT in inherently hard reasoning tasks such as 3-SAT, QBF, and Einstein’s Puzzle, achieving higher accuracy and faster convergence.
- Theoretical analysis shows that PENCIL achieves Turing completeness with optimal time and space complexity, making it efficient for solving arbitrary computable tasks.
- The proposed reasoning paradigm opens up possibilities for fine-tuning LLMs with memory-efficient capabilities, inspiring reexamination of existing reasoning models.
Read Full Article
17 Likes
For uninterrupted reading, download the app