Smarter AI: Reflection 70B Detects and Fixes Errors
Meet Reflection 70B: the strongest open-source model that surpasses GPT-4o & Claude 3.5! Self-correcting AI with top benchmark performance
The world’s strongest open source model: Reflection 70B . It is trained using a technique called “ Reflection-Tuning “, which teaches the model to find its own mistakes during reasoning and correct itself. Reflection 70B surpasses top closed source models (such as GPT-4o and Claude 3.5 Sonnet) on multiple benchmarks (MMLU, MATH, IFEval, GSM8K) and beats Llama 3.1 405B.
The model improves the effectiveness of chain thinking (CoT) by separating the planning process into independent steps and ensures that the output is concise and clear. In addition, the development team ensures the decontamination of the data.
The Reflection 70B weight has already been released, and the 405B version will be available next week, which is expected to improve performance further.
Features of Reflection 70B
1. Reflection-Tuning
The model introduces reflective tuning technology , which enables it to detect and correct its own reasoning errors during the reasoning process. This feature helps the model proactively identify problems and make corrections before generating the final answer, thereby improving the accuracy of the answer.
When the model generates an answer, it outputs its reasoning and <thinking> surrounds the thought process with special tags (such as ).
When the model detects an inference error during inference, it marks <reflection> the error with a label and corrects itself. This feature enhances the reliability of the model, especially when dealing with complex problems.
This enables the model to dynamically adjust its answers, reducing errors and ensuring higher accuracy.
2. Separation of reasoning process
When the model generates an answer, it separates the reasoning process from the final answer, using <thinking><output> the label to output the reasoning content and the label to output the final answer. This separation method improves transparency and allows users to clearly understand the reasoning logic of the model.
The model is particularly good at handling complex reasoning tasks. By using system prompts, the model is able to effectively complete highly logical queries and provide accurate and reflective answers.
3. Compatible with Llama 3.1 chat format
The model is trained based on the Llama 3.1 70B InstructLlama 3.1 and uses the standard chat format. This means that users can use this model like other Llama models, and its training process also adds some special tags to enhance reasoning and reflection capabilities.
4. Customizable system prompts
Reflection Llama-3.1 70B uses system prompts to guide the model’s reasoning and self-reflection. Users can adjust these prompts to customize the model’s behavior as needed. For example, prompt the model to think carefully or proactively correct errors when they occur.
5. Special training data
The model was trained using synthetic data generated by Glaive, which helped improve the model’s reasoning capabilities in a variety of tasks.
Prompt
The system prompt used for training this model is:You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at at any point, correct yourself inside <reflection> tags.You are a world-class AI system capable of complex reasoning and reflection. Please think about the problem in the <thinking> tag, and then provide your final answer in the <output> tag. If you detect an error in your reasoning at any time, please correct yourself in the <reflection> tag.- ”World-Class AI System”: This part tells the model that it has a high level of reasoning and reflection capabilities, thereby activating its complex reasoning capabilities.
- Reasoning process
<thinking>: The model is required - Self-correction
<reflection>: If the model detects an error during inference, it will - Final answer
<output>: The model provides the final answer in the tag after inference and possible corrections .
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