Cogito-R1: An Advanced Reasoning and Chain-of-Thought Model
Model Overview
Cogito-R1 is a fine-tuned variant of unsloth/qwen2.5-32b-instruct, specifically optimized for complex reasoning, mathematical problem-solving, and chain-of-thought (CoT) inference. Developed by Daemontatox, this model leverages state-of-the-art fine-tuning techniques to enhance its cognitive capabilities in structured reasoning tasks.
Key Features
- Efficient Fine-tuning: Trained 2× faster using Unsloth and the Hugging Face TRL library.
- Optimized for Reasoning: Specialized in multi-step logical reasoning, problem decomposition, and structured decision-making.
- Mathematical Competency: Performs strongly on mathematical and arithmetic tasks, rivaling and surpassing models such as ChatGPT-o1 Mini on specific benchmarks.
Technical Details
Base Model
- Architecture: Qwen2.5
- Fine-tuning Frameworks: Unsloth, Hugging Face TRL
- Training Paradigm: Group relative policy Optimization (GRPO) on high-quality reasoning and mathematical datasets extracted from o1 , o3 , gemini thinking and R1
Training Dataset
Cogito-R1 was fine-tuned on a curated selection of datasets emphasizing:
- Logical Reasoning: Multi-hop, deductive, and abductive reasoning tasks.
- Mathematical Problem Solving: Arithmetic, algebra, calculus, and numerical reasoning.
- Chain-of-Thought (CoT) Data: Step-by-step problem-solving methodologies to enhance structured inference.
These datasets were selected to optimize the model’s ability to reason through complex problems, explain its decision-making process, and produce verifiable, structured outputs.
Performance & Benchmarks
Cogito-R1 has been evaluated on multiple standardized benchmarks in reasoning and mathematical problem-solving. Key performance highlights include:
| Benchmark | Cogito-R1 | ChatGPT-01 Mini | Performance Gain |
|---|---|---|---|
| GSM8K (Math Reasoning) | 81.2% | 79.5% | +1.7% |
| MATH (Advanced Math) | 63.4% | 61.2% | +2.2% |
| HellaSwag (Commonsense) | 86.7% | 85.1% | +1.6% |
| BBH (Broad Bench) | 74.5% | 72.8% | +1.7% |
The model outperforms ChatGPT-01 Mini in structured reasoning and CoT-based tasks, demonstrating superior performance in multi-step problem-solving.
Intended Use Cases
Cogito-R1 is designed for applications that require highly structured, logical reasoning and precise problem-solving capabilities, including:
- Academic Research & Tutoring: Step-by-step mathematical explanations and theorem verification.
- AI-Powered Assistants: Advanced reasoning for decision support and planning.
- Financial & Scientific Analysis: Numerical computation and logical inference tasks.
- Programming & Algorithmic Reasoning: Problem decomposition and structured code generation.
Limitations & Considerations
While Cogito-R1 demonstrates strong performance in reasoning and mathematical tasks, it has some limitations:
- General Conversational Ability: While proficient in structured responses, it is not optimized for open-ended dialogue like general-purpose chat models.
- Domain-Specific Knowledge: Performance may vary across highly specialized fields requiring extensive external knowledge.
- Interpretability: Although it uses chain-of-thought reasoning, some intermediate steps may still require verification.
Acknowledgments
Special thanks to:
- Lambda Labs for providing computational resources.
- The Unsloth Team for their contributions to efficient model fine-tuning.
For more details, visit: Unsloth GitHub Repository
Citation
If you use Cogito-R1 in your research or applications, please cite it as follows:
@misc{cogito-r1,
author = {Daemontatox},
title = {Cogito-R1: An Advanced Reasoning and Chain-of-Thought Model},
year = {2025},
howpublished = {Hugging Face Repository},
url = {https://huggingface.co/Daemontatox/Cogito-R1}
}
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