Deepseek vs the rest: its not the GPU count that matters!

Introduction: The Paradox of Knowledge and Reasoning

To borrow the famous quote from James Carville and re-fashion it for the AI era: It's the reasoning, stupid!

AI development is grappling with a fascinating paradox: should we teach machines everything we know first and then help them reason, or should we let them reason first and allow knowledge to emerge dynamically? This fundamental question is reshaping how we think about designing intelligent systems.

On one hand, the dominant approach—used by OpenAI’s GPT-4, Meta’s LLAMA, Anthropic’s Claude, and DeepMind’s Gemini—relies on the premise that knowledge is foundational. These models are trained on massive datasets to develop encyclopedic knowledge bases, which serve as a static reservoir of facts. Reasoning, in this view, is a layer added later, refined through techniques like Reinforcement Learning from Human Feedback (RLHF) or supervised fine-tuning to align the model’s outputs with user expectations, ethics, and safety. This knowledge-first paradigm has undeniably delivered breakthrough results, enabling these models to perform across a staggering array of tasks.

Yet, DeepSeek-R1 offers a bold counterargument: What if reasoning is the foundation, and knowledge is simply a byproduct of reasoning? This reasoning-first approach shifts the focus from pretraining on massive datasets to enabling models to think. It posits that reasoning capabilities can emerge autonomously, driving the acquisition of knowledge in a task-specific, dynamic way. DeepSeek-R1 uses reinforcement learning (RL) to develop reasoning from scratch, skipping the traditional reliance on supervised pretraining, and lets the model grow its knowledge as it solves problems.

This paradox—knowledge first, reasoning later vs. reasoning first, knowledge later—is no mere change in approaches. It cuts to the core of how we build AI systems, how we measure their capabilities, and how we define progress toward Artificial General Intelligence (AGI). On one side is the comfort of structured knowledge guiding reasoning; on the other, the daring prospect of a system that learns to reason, adapt, and evolve without predefined boundaries.

This article explores this tension, comparing the dominant knowledge-first approaches with the revolutionary reasoning-first methodology of DeepSeek-R1. It’s a story of two competing visions, each with its own promise—and its own limitations—for the future of AI.

GPU Count: A Distraction in the AI Arms Race

The race to build bigger, better AI models often gets reduced to one metric: GPU count. We’ve seen headline after headline celebrating the sheer computational power behind state-of-the-art models—tens of thousands of GPUs in vast clusters, pushing the limits of parallel processing. While this metric is an impressive display of engineering prowess, it risks becoming a distraction from the real question:

Are we advancing intelligence or just brute-forcing complexity?

Once replication proves that reasoning-first systems like DeepSeek-R1 can achieve significant emergent capabilities with fewer resources, the GPU count arms race will appear less like progress and more like a costly scale exercise - Hindenburgs of 21st century.

Models That Learn Knowledge First and Then Align Reasoning

Contemporary large language models (LLMs) such as OpenAI's GPT-4o series, Meta's LLAMA series, Anthropic's Claude models, and Google DeepMind's Gemini follow a well-established paradigm. These models primarily acquire knowledge during pretraining by ingesting massive datasets and then align reasoning capabilities during fine-tuning, typically using supervised learning or reinforcement learning from human feedback. This sequential approach ensures that reasoning operates within the boundaries of pre-trained knowledge, optimizing safety, alignment, and utility.

• OpenAI GPT-4 employs a massive pretraining phase on a diverse corpus, creating a broad knowledge base. Reasoning capabilities are later enhanced through Reinforcement Learning from Human Feedback (RLHF) to ensure alignment with user preferences and safety protocols. GPT-4 emphasizes using pre-trained knowledge to structure reasoning aligned with ethical standards and coherent outputs.
• LLAMA models (Meta) adopt a similar knowledge-first approach but focus on efficiency and modularity. Techniques like low-rank adaptation (LoRA) allow targeted fine-tuning of reasoning capabilities while maintaining the foundational knowledge structure. This framework ensures robust reasoning within diverse applications without compromising pre-trained knowledge.
• Claude models (Anthropic) implement Constitutional AI, where reasoning is aligned with a predefined "constitution" of rules and safety principles. The pre-trained knowledge is further refined to reason safely and ethically across contexts, ensuring the outputs are interpretable and aligned with ethical guidelines.
• Gemini models (Google DeepMind) are known for their multimodal capabilities, synthesizing reasoning across text, images, and videos. Reasoning alignment is achieved through attention-guided multi-task learning, where the model learns to reason effectively across various input modalities.

In all these cases, reasoning is treated as a refinement layer built on top of the extensive knowledge acquired during pretraining. This two-step pipeline emphasizes a static knowledge base that informs and bounds reasoning capabilities, ensuring safety, coherence, and adaptability.

DeepSeek-R1: Learning Reasoning First

DeepSeek-R1 flips this paradigm on its head. Instead of treating reasoning as a refinement of pre-trained knowledge, it places reasoning front and center, allowing knowledge to emerge dynamically during reasoning tasks. This radical approach has far-reaching implications for how AI models are trained and deployed.

DeepSeek-R1 is built using pure reinforcement learning (RL), a method that encourages the model to evolve its reasoning capabilities autonomously. The process begins with DeepSeek-R1-Zero, where reinforcement learning is applied to a base model without any supervised fine-tuning (SFT). Using a tailored RL algorithm called Group Relative Policy Optimization (GRPO), the model is exposed to tasks requiring multi-step reasoning, logical deduction, and causal inference. These tasks act as a proving ground for the model to develop core reasoning skills, which are further refined during the process.

How DeepSeek-R1 Works

DeepSeek-R1’s training pipeline is broken into three distinct stages, and this is where the magic lies:

Stage 1: DeepSeek-R1-Zero – Reasoning Without Pretraining The model is trained on reasoning tasks using RL from scratch, without relying on pre-trained knowledge. This stage uses GRPO, where outputs are sampled in groups, and rewards are calculated based on their performance. The reward system is simple yet powerful, focusing on two key aspects:

1. Accuracy Rewards: Correctness of answers on deterministic tasks like math or coding problems.
2. Format Rewards: Ensuring the reasoning process is structured and traceable (e.g., reasoning is enclosed in <think> tags).

As training progresses, the model begins to exhibit emergent behaviors like self-reflection and the ability to revisit and refine earlier reasoning steps. This “aha moment” during training highlights the model’s capacity to autonomously develop sophisticated problem-solving strategies.

The Deepseek paper describes a fascinating moment when the model stopped the line of thinking and reasoning in its tracks, and recalibrated its line of reasoning grounds up, without being prodded along by any external stimuli. Preternatural!

Stage 2: Cold Start with Supervised Fine-Tuning To address limitations in readability and language mixing observed in R1-Zero, the team introduces a small, high-quality dataset of reasoning examples. This cold-start data serves as a foundation, allowing the model to learn structured reasoning formats and improve its initial performance. Human annotators curate these examples to ensure clarity and coherence, creating a baseline for further refinement.

Stage 3: Reasoning-Oriented Reinforcement Learning The final stage combines RL with the cold-start checkpoint. This step focuses on reasoning-intensive tasks like mathematics, coding, and logic, while also addressing human-readability issues through a language consistency reward. By the end of this stage, the model achieves competitive performance on benchmarks like AIME and MATH-500, rivaling OpenAI’s models in reasoning while being more flexible and adaptive.

Why DeepSeek's Novel Approach Matters

The implications of DeepSeek-R1’s approach are profound. First, by focusing on reasoning first, the model can dynamically generate knowledge tailored to specific tasks rather than relying on static embeddings. This makes it more adaptable, particularly in situations where pre-existing knowledge might be incomplete or irrelevant.

Second, the pipeline demonstrates that reasoning capabilities can emerge autonomously without extensive supervised fine-tuning. This reduces the reliance on massive labeled datasets, which are expensive and time-consuming to produce.

Finally, DeepSeek-R1 opens the door for smaller, more efficient models through distillation. By transferring reasoning patterns from larger models like DeepSeek-R1 to smaller ones (e.g., Qwen and LLAMA variants), the team has created dense models that outperform even larger competitors in reasoning tasks.

A Closing Thought: Are We Designing AI for Emergence—and Is AGI Built on It?

DeepSeek-R1’s reasoning-first paradigm invites a profound question: Are we now designing models specifically to foster emergence? Historically, emergent behaviors in AI—capabilities that arise without being explicitly programmed—were often treated as happy accidents in pretraining. But with DeepSeek-R1, emergence becomes intentional. By creating an environment where reasoning evolves through reinforcement learning, the model demonstrates sophisticated behaviors like self-reflection, error correction, and even dynamic knowledge synthesis.

This shift suggests we may be at the threshold of a new era in AI development, where emergence isn’t just a byproduct but the core design philosophy. Reinforcement learning, when combined with reasoning-first principles, transforms models into adaptive systems capable of independently evolving their abilities. This isn’t merely about improving task performance; it’s about fostering systems that learn how to learn—mirroring a key component of human intelligence.

If emergence can be intentionally nurtured through reasoning-first approaches like DeepSeek-R1, then perhaps the road to AGI lies here. AGI, after all, isn’t just about possessing vast knowledge—it’s about adapting, reasoning, and evolving in real time. Models like DeepSeek-R1 suggest that achieving AGI might not require massive static datasets but rather dynamic systems trained to adapt, reason, and create knowledge from scratch.

So, are we designing for emergence? We might be heading there. The current SOTA LLMs know all that is to be known. And yet, AGI is not here now. And if AGI is to become a reality, it may well be built on this foundation—where intelligence is no longer about what a system knows but about how it reasons and grows.