DeepSeek R1 and Model Distillation: Breaking It Down

DeepSeek R1 is a cutting-edge AI model designed to solve problems step by step, much like a human would. However, unlike traditional models that mostly learn from human examples, R1 takes it further — it teaches itself through trial and error using reinforcement learning (RL).

But what does that mean? Let’s break it down.

How DeepSeek R1 Learns

R1 trains itself in two key ways:

1. Trial and Error (Reinforcement Learning — RL): Instead of just memorizing solutions, R1 experiments with different approaches, learning from mistakes and improving over time.
2. Learning from Human Examples: While R1 mostly teaches itself, it also learns from human-provided examples to make its responses sound more natural and logical.

How R1 Solves Problems

Once trained, R1 doesn’t search for better answers in real-time. Instead, it just follows the steps it learned during training.

• This is efficient but can be a limitation for complex problems, where a human might stop, rethink, or try a new approach.
• R1’s fixed approach means it might struggle with problems that require extra reasoning or deep strategic thinking.

What Are Derivational Traces?

When R1 solves a problem, it creates step-by-step explanations, known as derivational traces — basically, a record of how it reached the final answer.

• These traces don’t actually make R1 smarter, but they help it look more logical and human-like.
• The risk? People might trust it more just because the reasoning sounds structured, even if the answer is wrong.

Distillation: Creating Smaller Models

Here’s where things get interesting. Instead of training new, smaller AI models from scratch, researchers train them using R1’s derivational traces.

• This process is called distillation, where a bigger model’s knowledge is transferred to a smaller one.
• While this makes the smaller models efficient, it also carries over any flaws in R1’s reasoning — meaning errors can get passed down.

Why This Matters

DeepSeek R1 is powerful, but its approach has trade-offs.

• It learns without direct human supervision, which is great for scalability but also introduces risks.
• Its structured explanations might give a false sense of reliability, leading to over-trust.
• The distillation process means flaws in reasoning don’t just stay in one model — they get inherited by smaller models too.

In short, while R1 brings exciting advancements, it’s essential to understand its limitations and not blindly trust AI-generated reasoning.