1. Reasoning Chain Function: g1 uses the Llama-3.1 model to solve usually difficult logic problems through dynamic chain reasoning (Chain of Thought). The model uses step-by-step reasoning and multi-method verification to improve the analytical ability and logical reasoning to solve logic problems.

2. Multi-method reasoning: The model is asked to use at least 3 different methods to come up with an answer and explore multiple possibilities to ensure that the model can solve the problem correctly. This strategy helped Llama-3.1 improve its accuracy on the Strawberry problem from 0% to 70%.

3. User visualization: Users can see the title and content of each step of the reasoning process to help understand the reasoning logic of the model.

4. JSON format output: Each step of the model's reasoning output is displayed in JSON format, including the title, reasoning content, and next action (continue or give a final answer).

How to Improve Logical Reasoning by G1

1. Dynamic Chain of Thought: g1 uses the principle of dynamic chain of thought to gradually guide the Llama-3.1 model to complete complex logic problems. Each time the problem is solved, the model will not give a direct answer, but reason step by step. Each step has a clear title and content to ensure that the reasoning process is visualized and structured.

2. Multi-step reasoning: In g1, the Llama-3.1 model is prompted to use at least 3 different reasoning methods to solve the problem. This multi-step reasoning process allows the model to explore different solution paths and avoid the final answer being wrong due to early reasoning errors. For example, in the "How many Rs are there in strawberry" problem, the model is guided to break down the word step by step and carefully examine each letter.

3. Iteration and self-verification: The model re-examines previous judgments at each reasoning step and verifies them using new methods as needed. This iterative self-verification mechanism helps ensure the accuracy of reasoning and avoid simple errors.

4. Output in JSON format: The results of each inference step are output in JSON format, including:
• title: Description of the operation of the current step.
• content: The specific reasoning details of this step.
• next_action: Indicates whether the model should continue reasoning or provide a final answer.

5. Hint strategy: G1's hint strategy optimizes the reasoning process of the Llama-3.1 model. By reminding the model to use multiple methods to explore problems and constantly reflect on previous reasoning, g1 improves the overall reasoning performance of the model. This hint includes asking the model to "re-examine and use new methods" and "use best practices."

Examples of G1

How many Rs are in strawberry?

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