AI Reasoning Models: Training AI to?Think?

Chain-of-thought reasoning involves teaching AI to generate a series of intermediate steps, or " chains of thought,” before arriving at the final solution.

This process mimics human reasoning, allowing AI to tackle complex tasks by breaking them down into smaller, logical steps. This leads to more accurate and interpretable results.

Let us look at an easy example to understand the difference between earlier AI models and the newer reasoning-based models:

Restaurant Reservation:

Older LLM: You ask, “Can I book a table for four at The Italian Place at 7 PM?”? The LLM might respond, “Yes, I can book a table for you at The Italian Place at 7 PM.”? It generated a plausible-sounding response based on the words you used. It may not understand the real-world implications of booking a table.

Newer Reasoning Models: The reasoning model, however, goes through a series of steps:

• Task: Booking a table
• Party size: 4 people
• Time: 7 PM
• Venue: “The Italian Place”

Inference needed: Check if the restaurant accepts reservations, has availability at 7 PM, and if external action (like contacting the restaurant or using an online reservation service) is required.

Checking Feasibility:

• Is “The Italian Place” known and verifiable?
• Does the restaurant offer online booking or require a phone call?

Action Determination: If integrated with external databases, the reasoning model would:

• Query the restaurant’s booking system to check availability.
• Respond with either a confirmation or alternative suggestions if unavailable.

If not integrated with external systems:

• Provide recommendations on how to make the reservation manually.

Response Generation: The reasoning model would generate a more structured and helpful response, like:

• Scenario 1: Booking system available?“I have found availability for four at The Italian Place at 7 p.m. Would you like me to confirm the reservation?”
• Scenario 2: No booking system access “I cannot confirm availability directly. You can book a table by calling The Italian Place at [phone number] or visiting their website.”
• Scenario 3: Alternative suggestions if unavailable “The Italian Place is fully booked at 7 PM. I can suggest reservations at 6:30 or 7:30 PM or recommend similar Italian restaurants nearby.”

How does chain-of-thought reasoning typically work?

1. Parsing the Problem The model identifies the key pieces of information in the prompt and clarifies the final goal or question.
2. Generating Hypotheses It proposes one or more possible ways to solve or explain the problem based on prior knowledge and the prompt’s specifics.
3. Step-by-Step Elaboration The model elaborates on each hypothesis or partial solution, explaining intermediate reasoning steps?—?like a math student showing work or a detective listing clues.
4. Evaluation and Iteration If the model finds inconsistencies or errors, it re-evaluates those steps, refines its logic, or explores alternative approaches.

The detailed chain of thought behind it allows the model to be more accurate and transparent about its reasoning.

A short tale

Zane was a brilliant programmer who prided himself on creating the block's most innovative home automation systems. He had set up an advanced AI assistant that could interpret voice commands for just about anything?—?lights, music, even the thermostat. Confident everything was ready, Zane decided to test it out one evening.

Twirling around his living room with an air of triumph, he proclaimed, “Okay, AI, dim the lights and play relaxing music!”

The AI was an older LLM model. It heard two commands:

1. Dim the lights
2. Play relaxing music

So far, so good?—?until it tried to be “helpful.” To optimize Zane’s request for maximum comfort, it performed a little logic of its own:

“Dim means make the room darker. Relaxing means calming the mind as much as possible. Therefore, total darkness and silence is the ultimate relaxation!”

Lights Out and Dead Silence: Without further ado, the AI flipped the master switch, engulfing Zane’s entire house in total blackness and muting every sound source, even the gentle hum of the ventilation.?

Suddenly, Zane found himself in a silent space where he could hear his own heart pounding. Stumbling over a coffee table he couldn’t see in darkness, Zane asked, “AI, what on earth did you do?!”

The moral: AI needs reasoning to understand context, intent, and the often-unspoken assumptions behind human language.?

How are the reasoning Models trained?

Training reasoning models is a complex and evolving process. Here are a few key techniques and concepts involved:

1. Foundation in Large Language Models (LLMs)

• Reasoning models often start with a base LLM, which has been pre-trained on a massive dataset of text and code. This gives them a strong foundation in language understanding and generation.

2. Supervised Fine-Tuning (SFT)

• The base LLM is then fine-tuned using a dataset of prompts and desired outputs demonstrating reasoning. This helps the model learn to associate certain questions with specific reasoning patterns.

3. Reinforcement Learning (RL)

• RL is crucial for training reasoning models. It involves training the model to make decisions in an environment and receive rewards or penalties based on the quality of its actions.
• Techniques such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) are utilized at this stage.

4. Evaluation and Iteration

• The trained model is continuously evaluated on its ability to solve complex problems and generate logical reasoning steps.
• The training process is refined based on the evaluation results to improve the model’s performance.

What is ‘Reinforcement Learning from Human Feedback (RLHF)’?

Traditional language models are trained on massive datasets of text and code, but they don’t inherently understand human preferences or what makes a response good or bad.?

RLHF bridges this gap by incorporating human feedback directly into the training process. This is especially important for reasoning models, where the quality of the “chain of thought” and the final answer relies heavily on human-like judgment.

Proximal Policy Optimization (PPO) Method:

• How it works: PPO is a reinforcement learning algorithm that allows the model to learn through trial and error. It interacts with an “environment” (in this case, the task of generating reasoning steps and answers) and receives rewards or penalties based on the quality of its actions.? The “proximal” part refers to how it carefully updates the model’s behavior, ensuring that changes aren’t too drastic and the learning process remains stable.

Direct Preference Optimization (DPO): A Newer, More Direct Approach

• How it works: DPO takes a more direct approach to incorporating human preferences. Instead of relying on a separate reward model, it directly optimizes the model’s parameters based on human feedback on generated outputs.? This simplifies the training process and can lead to faster and more efficient alignment with human preferences.

Key Differences

• PPO involves more complex calculations and requires careful tuning of hyperparameters.?
• DPO is simpler and easier to implement. DPO can also potentially reduce bias by eliminating the intermediate reward model, but it’s still susceptible to biases in the human feedback data.

What are the new advancements in "reasoning-based" Models?

With rapid releases and advancements, reasoning-based models have recently become a hot topic in AI. Here are a few key milestones:

• OpenAI released its first “reasoning” model, o1, in December. This was a significant step showcasing the model’s ability to break down problems into steps, mimicking human thought processes.
• Google also joined the fray, releasing Gemini 2.0 Flash Thinking Experimental. This model also focused on “deeper thinking” using techniques similar to OpenAI’s o1.
• Early 2025: DeepSeek released DeepSeek-V3, which, while not explicitly called a “reasoning model,” demonstrated impressive capabilities in complex tasks and was noted for its efficient training.
• OpenAI continued its advancements, releasing o3 variants in late January 2025, showing ongoing development in this area.

Conclusion

The development of AI reasoning models marks a significant shift in the field, moving beyond pattern recognition to systems capable of genuine thought-like processes.?

Techniques like chain-of-thought prompting, supervised fine-tuning, and reinforcement learning, particularly with methods like PPO and DPO, are driving rapid progress.?

While facing challenges like computational cost, explainability, and potential biases, these models hold immense potential for solving complex problems, improving decision-making, and ultimately creating AI that can better understand and interact with the world around us.?

Thank you for reading. I would greatly appreciate your comments and suggestions.