The Reasoning LLM Revolution: (Deepseek R1 & OpenAI O3) - What Product Managers Need to Know

Who is this blog for?

As a machine learning product manager, I’ve read countless ML blogs and papers—some dive too deep into technical details catering to? data scientists and researchers, while others stay too high-level targeting managers. This blog aims to bridge that gap, offering product managers a clear understanding of the impact of reasoning LLMs without overwhelming complexity. Opinions shared are my own and not those of my employer.

In this blog, I will cover:

• What are Reasoning LLMs
• What is Reasoning?
• Why Traditional LLMs Fall Short
• How Reasoning LLMs Work
• Major Features of Reasoning LLMs
• When to Use Reasoning LLMs
• Example Use Case: AI-Powered Medical Diagnosis Assistance
• Major Reasoning LLMs
• Potential Problems with Reasoning LLMs

What are Reasoning LLMs?

Traditional AI models can generate text, answer questions, and even mimic human conversation—but when it comes to deep reasoning, they often fall short. Enter Reasoning LLMs like OpenAI-O3 and DeepSeek R1—a new breed of AI designed to think, analyze, and solve problems with logic, not just pattern-matching.

From deductive logic and mathematical proofs to commonsense reasoning and cause-and-effect analysis, these advanced models are transforming how AI tackles complex challenges. Imagine an AI that can solve intricate puzzles, make strategic decisions, and predict outcomes based on evidence—that’s the power of reasoning-driven AI.

In this blog, we’ll explore what makes Reasoning LLMs different, why they matter, and how they’re reshaping AI’s future. Let’s dive in!?

What is Reasoning?

In the context of LLMs reasoning refers to the ability of the model to solve problems, make inferences, and draw conclusions based on the information it has been given. It involves going beyond simple pattern recognition and applying logical principles to arrive at a solution.??

The following are some of the main types of reasoning that reasoning LLMs can perform but traditional LLMs struggle with:

• Inductive Reasoning: This involves forming general conclusions from specific observations or examples. For instance, if an LLM observes that many birds can fly, it might inductively conclude that all birds can fly. While this type of reasoning is not always guaranteed to be accurate, it can be useful for making predictions or generalizations.??
• Deductive Reasoning: This involves drawing specific conclusions from general premises or rules. For example, if an LLM knows that all men are mortal and that Socrates is a man, it can deductively conclude that Socrates is mortal. This type of reasoning is often used in logical puzzles or mathematical proofs.??
• Abductive Reasoning: This involves inferring the most likely explanation for a given set of observations. For example, if an LLM observes that the ground is wet, it might abductively conclude that it has rained. This type of reasoning is often used in diagnostic or troubleshooting scenarios.??

Why Traditional LLMs Fall Short

Traditional LLMs, like GPT-4o, excel at generating creative text formats, translating languages, and answering your questions in an informative way, but they often struggle with tasks that require complex reasoning. Reasoning LLMs, on the other hand, are designed to be more transparent and methodical in their approach. They can explain the "why" behind their answers, handle ambiguity, and solve complex problems that require logical deduction or multi-step reasoning.

How Reasoning LLMs Work

Reasoning LLMs achieve their capabilities through a combination of advanced techniques:

• Chain-of-Thought (CoT): CoT encourages the model to break down problems into smaller steps and explicitly state its reasoning process. This helps the model think more systematically and avoid making illogical jumps or assumptions.??
• Reinforcement Learning (RL): RL techniques are used to train the model to refine its reasoning strategies, recognize and correct its mistakes, and try different approaches when one isn't working. This allows the model to learn from its mistakes and improve its reasoning abilities over time.??
• Inference-Time Scaling: This involves increasing computational resources during inference to improve output quality. By giving the model more time and resources to "think," it can generate more accurate and comprehensive responses.

Major Features of Reasoning LLMs

Reasoning LLMs exhibit several key features that distinguish them from traditional LLMs:

• Chain-of-Thought Reasoning: They break down complex problems into smaller, more manageable steps, making their reasoning process more transparent and easier to follow.??
• Self-Verification: Some reasoning LLMs can verify their answers by re-evaluating their reasoning steps, improving accuracy and reducing errors.??
• Adaptability: They can adapt their reasoning strategies based on the specific task or context, allowing them to handle a wider range of problems.??
• Explanation Generation: They can provide explanations for their answers, making their reasoning more understandable to humans.??

When to Use Reasoning LLMs

Reasoning LLMs are particularly valuable when:

• Solving Complex Problems: When faced with tasks that require multi-step reasoning, such as solving puzzles, mathematical equations, or logical deductions.
• Multi-Step Processes: For workflows that involve multiple interconnected steps, such as complex customer service inquiries or detailed data validation.
• Explaining the "Why" Behind an Answer: In situations where understanding the reasoning process is crucial, such as in educational settings or when building trust in AI systems, reasoning LLMs provide valuable insights into how they arrive at a solution.
• Inference and Decision-Making: When you need to draw conclusions from complex information, like in intricate reading comprehension or strategic decision-making.??
• Handling Ambiguity: When dealing with ambiguous queries or situations where multiple interpretations are possible, reasoning LLMs can systematically explore different possibilities and arrive at the most likely solution.

Example Use Case: AI-Powered Medical Diagnosis Assistance

A doctor is trying to diagnose a patient with fatigue, weight loss, and stomach pain. Traditional AI models can list possible diseases based on symptoms, but they lack reasoning skills to connect different factors like medical history, lab tests, and lifestyle.

A reasoning LLM can think through the problem step by step, just like a real doctor would.

Step 1: Understanding the Symptoms & Patient History

• The model gathers all patient details: age, diet, previous illnesses, medications, and family history.
• It looks for patterns and checks if symptoms are related to past conditions.

Example Output:

• “The patient has lost 10 lbs in 2 months, has a history of acid reflux, and reports feeling tired most days.”

Step 2: Identifying Possible Causes Using Logical Reasoning

• Instead of listing every possible disease, the model narrows it based on likelihood.
• It uses causal reasoning to check if symptoms are linked.

Example Output:

• “Fatigue and weight loss could be caused by diabetes, thyroid issues, or digestive problems. Since the patient also has stomach pain, a digestive disorder like celiac disease or an ulcer is more likely.”

Step 3: Recommending Tests to Confirm Diagnosis

• The model can suggests the next steps to confirm the cause.
• It recommends blood tests, imaging, or diet tracking based on logic.

Example Output:

• “A blood test for iron levels and thyroid function can check for anemia or hypothyroidism.”
• “An endoscopy can help detect an ulcer or stomach inflammation.”
• “Try avoiding gluten for 2 weeks to see if symptoms improve (possible celiac disease).”

Step 4: Explaining Next Steps & Treatment Options

• The model summarizes what the doctor should focus on next.
• It offers treatment ideas based on the most likely diagnosis.

Example Output:

• Likely Cause: Stomach ulcer (needs confirmation).
• Next Steps: Prescribe a test for H. pylori bacteria. If positive, start antibiotics and acid reducers.
• Alternative Possibility: If symptoms don’t improve, check for celiac disease.

Why This Matters

A traditional LLM might just list diseases, but a reasoning LLM can:

• Think step by step, like a real doctor.
• Find the most likely cause instead of guessing.
• Suggest logical next steps to confirm the diagnosis.
• Give clear explanations to doctors and patients.

Major Reasoning LLMs

Several prominent reasoning LLMs are available:

• OpenAI's O Series: These models, including O1 and O3, are specifically designed for complex reasoning tasks. They excel in math, coding, and general knowledge domains. However, they can be more expensive than other LLMs.??
• DeepSeek R-1: This model is an open-source alternative to proprietary LLMs, known for its strong performance on complex reasoning benchmarks, particularly in math and coding. It's also relatively cost-effective compared to some other reasoning LLMs.
• Gemini 2.0 Flash:? This model is able to handle large input/output context windows and excels at multimodal understanding.??
• Other models: Sky-T1-32B-Preview (UC Berkeley), s1-32B (Stanford University) and Qwen2.5 family models (Alibaba)?

Potential Problems with Reasoning LLMs

While reasoning LLMs offer significant advantages, it's important to be aware of their limitations:

• Lack of True Understanding: Reasoning LLMs, while capable of impressive feats of logic, still lack a deep understanding of the world. They may struggle with tasks that require common sense, real-world knowledge, or nuanced interpretations of human language.??
• Contextual Limitations: While they can handle complex information, reasoning LLMs may sometimes lose track of context or struggle to maintain coherence over extended interactions. This can lead to inconsistencies or errors in their reasoning.??
• Overthinking and Verbosity: Reasoning LLMs can sometimes "overthink" problems, leading to unnecessarily complex or verbose responses. This can be inefficient and may even lead to incorrect answers.??
• Hallucinations: Like all LLMs, reasoning models are susceptible to hallucinations, where they generate plausible-sounding but incorrect information. This can be a significant concern in applications where accuracy is critical.
• Cost: Reasoning LLMs can be more computationally expensive than standard LLMs, which can affect product development and deployment decisions.
• Overkill for Simpler Tasks: For tasks that traditional LLMs already handle effectively, such as content generation, summarization, or basic question answering, the advanced reasoning capabilities of reasoning LLMs might be unnecessary.