Artificial Intelligence (AI) has evolved from simple rule-based systems to powerful deep learning models capable of reasoning, planning, and decision-making. However, traditional AI models often struggle with complex reasoning tasks that require multiple steps of logical deduction.

To bridge this gap, researchers have introduced Chain of Thought (CoT) reasoning, an approach that enables models to think step-by-step before making decisions. When combined with Reinforcement Learning (RL), this technique significantly enhances AI’s ability to solve complex problems efficiently.

What is Chain of Thought Reasoning?

Chain of Thought (CoT) is a technique that helps AI models break down complex reasoning tasks into smaller, logical steps before arriving at a final answer. This idea was popularized by Google’s research on large language models (LLMs) such as GPT-4 and PaLM.

For example, instead of answering:

“A car travels 60 km in 1 hour. How long will it take to travel 150 km?”

A traditional AI might instantly output “2.5 hours” without explanation.

A CoT-based AI, on the other hand, will reason step-by-step:

1. “The car’s speed is 60 km per hour.”
2. “To find the time, we divide the distance by speed.”
3. “150 km ÷ 60 km/h = 2.5 hours.”
4. “Final answer: 2.5 hours.”

By explicitly modeling intermediate steps, CoT improves accuracy, particularly in math, logical reasoning, and problem-solving.

Why Chain of Thought Makes AI Smarter

1. Better Logical Consistency – Instead of guessing, the AI follows structured thinking.
2. Explainability – CoT enables AI models to show their work, making outputs more transparent and verifiable.
3. Higher Accuracy on Complex Problems – Research shows that CoT improves performance on multi-step reasoning tasks like math word problems and commonsense reasoning.

Combining Chain of Thought with Reinforcement Learning (RL)

Reinforcement Learning (RL) is a technique where an AI learns by trial and error, receiving rewards for correct answers and penalties for mistakes. RL is widely used in robotics, game-playing AI (like AlphaGo), and fine-tuning large language models like ChatGPT.

When CoT reasoning is combined with RL, AI models not only think step-by-step but also improve their reasoning skills over time by optimizing for better performance.

How RL Enhances CoT Reasoning

1. Rewarding Correct Reasoning Paths – Instead of rewarding only the final answer, RL encourages models to produce logically sound intermediate steps.
2. Self-Improvement – AI models can learn from past mistakes, optimizing their reasoning process dynamically.
3. Generalization – RL+CoT helps models adapt to different problem types, making them more robust.

Example: Using RL with CoT in AI Chatbots

Imagine a customer support AI that answers refund requests. Without CoT, it might say:
“Sorry, I cannot process this refund.”

With CoT + RL, the AI can reason:

1. “The customer purchased an item 32 days ago.”
2. “The refund policy allows returns within 30 days.”
3. “Since 32 > 30, a refund is not allowed.”
4. “I should explain this politely and offer an alternative solution.”

Over time, reinforcement learning helps the AI refine its reasoning by optimizing responses based on customer satisfaction scores.

Real-World Applications of CoT and RL

Watch CoT in action

At present the best way to see CoT at work is in DeepSeek R1. Ask a qestion and observe Deepseek reosoning.

1. Mathematics & Scientific Research – AI can solve complex equations by breaking them down logically.
2. Legal & Financial Analysis – AI can assess contracts step-by-step to identify risks.
3. AI Assistants (ChatGPT, Bard, Claude, etc.) – These models use CoT for better reasoning in responses.
4. Medical Diagnosis – AI can reason through symptoms and medical history to suggest diagnoses.

Key Research Papers & References

• Chain of Thought Prompting Elicits Reasoning in Large Language Models (Google Research, 2022) – Read here
• Reinforcement Learning from Human Feedback (RLHF) in AI Fine-Tuning – Paper
• DeepMind’s AlphaZero: RL-driven decision-making – Link
• Applying RL in Large Language Models (LLMs) – OpenAI Blog

Conclusion

Combining Chain of Thought reasoning with Reinforcement Learning creates smarter, more reliable AI models. By encouraging step-by-step reasoning and learning from mistakes, this approach boosts accuracy, transparency, and adaptability.

As AI continues to evolve, CoT + RL will play a crucial role in making AI more human-like in reasoning—helping in fields like science, law, medicine, and education.

Want to explore CoT-based AI solutions for your business? Check out Evert’s Labs.

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