Think Out Loud: Rethinking AI with Reinforcement Learning

From raw, experimental models to refined, self-aware systems — discover how DeepSeek-R1’s approach is changing the game in AI.

10 min readFeb 1, 2025

DeepSeek has not only disrupted the entire AI landscape with its innovative approach, but it has also sent shockwaves through the stock market and reshaped how next-generation reasoning language models will be built. Unlike the compute-heavy architectures favored by giants like OpenAI, Google and Anthropic, DeepSeek-R1 leverages a leaner, reinforcement learning–based method to train its models. This new approach enables high-quality reasoning while using far less compute, paving the way for more efficient LLMs.

Before diving into DeepSeek-R1 itself, it’s important to understand the different types of language models and where distilled models fit in.

Understanding Types of Language Models

Full Models: Full models are the complete versions of a language model with every parameter intact. They are built using massive amounts of unstructured data and then refined through supervised fine-tuning with human-labeled examples. These models achieve state-of-the-art performance across a wide range of tasks but demand very high computational resources such as powerful GPUs, significant memory, and extended training time. They are best suited for environments where performance is paramount and resources are plentiful.

Distilled Models: Distilled models are streamlined, smaller versions of full models created through a process known as knowledge distillation. In this process, a large, complex model (the “teacher”) generates high-quality outputs that a smaller “student” model learns to mimic. Distilled models require much less computational power and memory while retaining most of the teacher model’s reasoning capabilities. They are ideal for real-time applications or deployments on devices with limited hardware, even though there may be some trade-offs in nuance and detail compared to full models.

Quantized Models: Quantized models reduce the precision of numerical values (for example, using 8-bit integers instead of 32-bit floats) to lower memory usage and speed up inference. This technique is often applied to distilled models to further optimize performance on constrained hardware. While quantization can lead to a slight drop in accuracy, it significantly enhances efficiency, making it a popular choice for deployment in resource-limited scenarios.

DeepSeek-R1: A New Approach to LLM Reasoning

Traditional language models are typically trained using a three-step process:

Pre-training: Learning general language patterns from vast, unstructured data.
Supervised Fine-Tuning (SFT): Refining the model on curated, human-labeled data.
Reinforcement Learning with Human Feedback (RLHF): Using reward signals from humans or models to further align outputs.

DeepSeek-R1 breaks the mold by taking an alternative path. Its earliest iteration, called DeepSeek-R1-Zero, skips the initial human-labeled SFT phase and relies solely on pure reinforcement learning (RL). The idea was to test whether advanced reasoning capabilities — particularly in tasks like math and coding — could emerge just from trial and error with rule-based rewards.

What Happens in DeepSeek-R1-Zero?

Learning from Rewards Alone:

The base model (DeepSeek-V3-Base) is exposed to a reward system that values correctness in code outputs, math solutions, and proper formatting.
Without prior guidance, the model explores various strategies, and its performance on tasks like the AIME math competition dramatically improves (from a pass@1 score of 15.6% to 71.0%).

Emergence of Advanced Behaviors: The model begins to develop extended chain-of-thought (CoT) reasoning and even demonstrates “aha moments,” where it pauses to correct its approach mid-answer.

Limitations:Although the model shows impressive reasoning abilities, its outputs can be messy — mixing languages or presenting awkward phrasing — making them less user-friendly.

Introducing a “Cold-Start” Supervised Dataset

Because pure RL training from scratch can be chaotic, the researchers introduced a small, high-quality supervised dataset — a “cold-start” dataset — to stabilize the learning process.

What Is a Cold-Start Supervised Dataset?

A limited collection of carefully curated examples (a few thousand rather than tens of thousands) that include questions, detailed reasoning steps, and correct answers.
It serves as an initial primer to ensure that the model’s outputs are readable and consistent.

Benefits of the Cold-Start Dataset:

Better Readability & Consistency: The model learns what clear, well-structured answers look like.
This guidance helps reduce language mixing and awkward phrasing.

Faster Convergence in Reinforcement Learning:

With a stable starting point, the model quickly learns useful patterns and improves its performance during subsequent RL training.

You can think of it as learning chess with a few opening moves already mastered, rather than playing endless random games. A small “openings guide” makes learning faster and more effective.

Reinforcement Learning for Reasoning: A Multi-Stage Pipeline

Stage 1: Pure RL on the Base Model (DeepSeek-R1-Zero)
The training begins with the base model (DeepSeek-V3-Base) using pure reinforcement learning. In this stage, the model generates multiple outputs for each prompt and is rewarded based on rule-based criteria focused on correctness, code outputs, or math solutions. This approach encourages the emergence of advanced reasoning behaviors like extended chain-of-thought and self-correction, even though the initial outputs may be messy and inconsistent.

Stage 2: Incorporating the Cold-Start Supervised Dataset
To stabilize the learning process, a small, high-quality set of curated examples is introduced. This cold-start dataset serves as an initial guide that demonstrates the desired structure, clarity, and coherence in responses. It ensures the model starts with a reliable baseline, reducing the chaotic nature of pure RL from the beginning.

Stage 3: First Reinforcement Learning (Reasoning-Focused)
With the guidance from the cold-start data, the model returns to reinforcement learning with a refined focus on reasoning quality. During this stage, rewards are adjusted to emphasize not only correctness but also language consistency and proper formatting. This helps the model produce more detailed and understandable chain-of-thought explanations.

Stage 4: Rejection Sampling for Additional Training Data
The model is prompted to generate more reasoning examples, after which a filtering process discards low-quality or incorrect outputs. This process, known as rejection sampling, uses rule-based checks or a smaller reward model to select only the best responses. The resulting high-quality data is then added to the training set, further enhancing the model’s performance.

Stage 5: Final Fine-Tuning (Combining SFT and RL)
In the final stage, the model undergoes a comprehensive fine-tuning process that combines supervised fine-tuning (SFT) on the newly generated high-quality examples with another round of reinforcement learning. This phase balances the model’s reasoning accuracy with user alignment factors such as helpfulness and harmlessness, ultimately producing DeepSeek-R1 — a model that is both logically robust and highly readable.

The “Aha Moments” and Reflective Reasoning

One of the most exciting discoveries during the training of DeepSeek-R1 was the emergence of self-reflective behavior. The model began to pause and review its work — essentially thinking out loud — which has significant benefits. Here’s why this “aha moment” matters:

Self-Correction: The model checks its work as it generates an answer, catching mistakes early so they don’t end up in the final output.
Better Accuracy: By fixing errors along the way, the overall performance of the model improves over time. The reinforcement learning process rewards these corrections, leading to more precise answers.
Transparent Reasoning: DeepSeek-R1 explains its thought process step by step, making it easier for users to follow how it reached its conclusions. This clear, “think aloud” approach has even influenced other companies, like OpenAI, to adopt similar techniques.

These self-reflective “aha moments” not only boost the model’s accuracy but also provide a window into its reasoning process, enhancing user trust and understanding.

Distillation: Smaller Models That Still Reason Well

While DeepSeek-R1’s reinforcement learning pipeline achieves groundbreaking reasoning, deploying its full-scale version remains impractical for many real-world applications. This is where model distillation bridges the gap, transforming the raw reasoning power of large models into compact, deployable formats.

How Distillation Complements DeepSeek-R1’s RL Approach

DeepSeek-R1’s lean RL methodology already reduces computational demands compared to traditional LLM training. Distillation takes this efficiency further:

The full DeepSeek-R1 model acts as a teacher, generating high-quality answers rich in chain-of-thought reasoning.
A smaller student model (e.g., DeepSeek-R1-Distill-Qwen-14B) is then trained to mimic these outputs, inheriting the teacher’s logical rigor while shedding computational bloat.

This process mirrors the efficiency gains seen in DeepSeek-R1-Zero’s RL training — both prioritize extracting maximum capability from minimal resources.

Cost Efficiency Without Sacrificing Reasoning

Distillation preserves the essence of DeepSeek-R1’s reasoning prowess while dramatically cutting resource demands. These compact models retain approximately 90% of the teacher’s performance on complex tasks like math and coding, all while using just 1/10th the GPU memory of their full-scale counterparts.

For instance, the DeepSeek-R1-Distill-Qwen-14B model achieves parity with GPT-3.5 on the GSM8K math benchmark despite being 12x smaller — a testament to how distillation captures the teacher’s logical rigor without its computational bulk.

Hardware Flexibility for Running Locally

Where full models like DeepSeek-R1 demand premium A100/H100 GPUs, distilled variants democratize access by running efficiently on consumer-grade hardware. A single RTX 3090 GPU with 24GB of VRAM can effortlessly host these models, eliminating the need for specialized infrastructure.

Setting Up DeepSeek-R1 Locally

For those who want to experiment with DeepSeek-R1 on their own machines, here are two popular tools:

Using LM Studio

Installation and Setup:

Download LM Studio from its official website.
Once installed, search for “DeepSeek” to find various models, including distilled versions.

Choose a model (for example, a distilled 14B variant) and check details like quantization levels.
Download the model, then start a chat session.
The interface displays both the final answer and the chain-of-thought behind it.

Using Ollama and ChatBox.AI

Installation and Command-Line Use:

Download and install Ollama from its website.
Run the desired model using:

ollama run deepseek-r1:14b

Start the server:

ollama serve

For better interaction, download ChatBox.AI and Configure Environment Variables:

Image by Author

Check the settings to confirm the model is up and running.

Start a new chat session in ChatBox. Ensure the model status shows “Connected” in the bottom toolbar.

By hosting models locally with Ollama and ChatBox or with LMStudio, you ensure data privacy, reduce latency, and gain full control over AI interactions. Ideal for developers, researchers, or privacy-focused users.

Deploying DeepSeek-R1 on Runpod.IO

For applications where local hardware isn’t sufficient or faster performance is needed, cloud services like Runpod.io are an excellent alternative.

Why Runpod.io?

Runpod.io gives you access to high-end U.S.-based GPU instances capable of running large models efficiently. This platform not only meets high performance and scalability needs but also provides enhanced data security and compliance with privacy regulations.

Scalability: Choose the instance size that best matches your workload and scale up when needed.
Enhanced Data Security: As a U.S.-based service, Runpod.io provides additional security benefits for sensitive applications.

Setting Up on Runpod.io

Account Creation and Instance Selection: Sign up on Runpod.io and select a GPU instance based (e.g A100 ) on your model’s memory and compute requirements.

Once you deploy the pod, you can connect to it via Web Terminal or with SSH:

Load the DeepSeek-R1 model or its distilled version into the container.

Starting the Model Server with vllm Library :

It’s an easy-to-use library for LLM inference and serving.

vllm serve deepseek-ai/deepseek-r1:671b --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager

Using vLLM with the flags “ — tensor-parallel-size 2”, “ — max-model-len 32768”, and “ — enforce-eager” distributes the model across two GPUs, enables processing of inputs up to 32,768 tokens, and runs in eager execution mode for immediate, predictable computation.

This launches the full R1 model server on your pod and is able to handle incoming API calls.

Interact with the model via API calls or a web interface.
Monitor performance on the Runpod.io dashboard and adjust instance resources as necessary.

Conclusion

DeepSeek-R1 represents a bold new direction in language model training. By combining pure reinforcement learning with a minimal cold-start dataset, it achieves advanced reasoning capabilities efficiently and cost-effectively. Whether you choose a full-scale model for cutting-edge research or a distilled version for everyday applications, DeepSeek-R1’s innovative multi-stage training and transparent “think aloud” approach offer a glimpse into the future of AI — one that is open, accessible, and efficient.

For individual developers, running distilled models on a PC is a viable option if you have a suitable GPU (often 6GB VRAM or more). However, these models may not match the accuracy of full models. For enterprise-level applications, hosting the full DeepSeek-R1 on secure platforms like Runpod.io is likely the best choice, ensuring top performance and data security.

Happy experimenting, and welcome to the new era of efficient, self-improving AI!