Eliciting Chain-of-Thought in Base LLMs via Gradient-Based Representation Optimization
Published:Nov 24, 2025 13:55
•1 min read
•ArXiv
Analysis
This article describes a research paper focused on improving the reasoning capabilities of Large Language Models (LLMs). The core idea involves using gradient-based optimization to encourage Chain-of-Thought (CoT) reasoning within base LLMs. This approach aims to enhance the models' ability to perform complex tasks by enabling them to generate intermediate reasoning steps.
Key Takeaways
Reference
“The paper likely details the specific methods used for gradient-based optimization and provides experimental results demonstrating the effectiveness of the approach.”