LLM Reasoning vs Reasoning LLM
- (Harvard University - Harvard Taiwan Student Association)
- Overview
"LLM Reasoning" refers to the ability of a Large Language Model (LLM) to process information, make logical connections, and generate coherent outputs based on reasoning skills, while "Reasoning LLM" specifically indicates an LLM that is specifically designed and trained to excel at complex reasoning tasks, often by employing a "chain of thought" process to show its intermediate reasoning steps in the response, going beyond basic pattern matching capabilities of a standard LLM.
Key Differences:
- Focus: "LLM Reasoning" is a broader term describing the reasoning capabilities within any LLM, while "Reasoning LLM" indicates an LLM specifically optimized for complex reasoning tasks.
- Intermediate Steps: A "Reasoning LLM" typically displays its thought process by showing the intermediate steps it took to reach a conclusion, whereas a standard LLM might just provide a final answer without explaining its reasoning.
- Application: While most LLMs can perform some basic reasoning, a "Reasoning LLM" is often used for tasks requiring more complex logical deduction, like solving puzzles, mathematical proofs, or navigating intricate scenarios.
[More to come ...]
