This story explores the shifting narrative of AI development using DeepSeek's reinforcement learning-driven approach as a lens to challenge the dominant paradigm of supervised fine-tuning. Drawing inspiration from Chimamanda Ngozi Adichie’s 'The Danger of a Single Story,' it critiques how performance benchmarks often reduce AI progress to a singular metric, overlooking adaptability and methodological diversity. Through a structured comparison of DeepSeek and OpenAI’s LLM pipelines, an analysis of financial market reactions, and a reflection on reductionist thinking, this piece invites readers to rethink the evolving AI landscape and the narratives that shape it.
I am a storyteller (an assembler if you will), and I would like to tell you a story about artificial intelligence—a field increasingly defined by "performance metrics." As a product strategist and beginner in AI studies, the recent news about DeepSeek left me questioning: Do our performance benchmarks miss the bigger picture?
Are the hardworking AI specialists having their efforts reduced to a single metric?
This is the story of a race that no one fully understands but everyone claims to predict.A race where headlines scream that America is falling behind in artificial intelligence.
"If I had to ponder the most authoritarian thing, it'd be rallying America behind a single metric."
And yet, like the markets themselves, the story is enough to sway billions.
DeepSeek’s unveiling of its reasoning-centric models, DeepSeek-R1-Zero and DeepSeek-R1, reshaped my understanding of AI’s developmental stages. Rather than following the traditional pre-training and SFT sequence, DeepSeek emphasized reinforcement learning (RL) from the outset.This alternative approach redefined the AI assembly process, demonstrating that intelligence could emerge through adaptive learning rather than prescribed data labeling. Each component of an LLM—whether model initialization, reasoning development, or alignment—took on new meaning under DeepSeek’s methodology.
Large Language Models (LLMs) undergo a hierarchical development process, moving through sequential stages that shape their intelligence, reasoning capabilities, and alignment with human preferences. DeepSeek and OpenAI have taken distinct technical approaches in structuring these stages, leading to notable differences in performance, adaptability, and efficiency.
DeepSeek-R1-Zero began exhibiting behaviors such as self-reflection and iterative problem-solving—abilities once thought to require heavy supervision. These emergent capabilities were powered not by traditional methods but by pure RL.
Despite the risks of bypassing a conventional training stage, DeepSeek achieved extraordinary results, boasting an impressive 71% Pass@1 accuracy on the challenging AIME 2024 benchmark—a near-miracle in the absence of supervised guidance (DeepSeek AI 5).
| Stage |
Component |
OpenAI (ChatGPT o1) |
DeepSeek (R1 & R1-Zero) |
| Model Initialization |
Foundation Model |
Pre-trained on massive datasets using supervised fine-tuning (SFT) |
Skips SFT entirely, initializing from a base checkpoint optimized through Reinforcement Learning (RL) |
| Supervised Fine-Tuning (SFT) |
Establishes reasoning baselines using labeled datasets and structured instruction tuning |
Not used; relies on RL to iteratively develop reasoning patterns |
| Reasoning Development |
Chain-of-Thought (CoT) Reasoning |
Enhances problem-solving by breaking complex tasks into step-by-step processes |
Develops reasoning autonomously through iterative RL learning, without pre-defined CoT prompts |
| Data Efficiency |
Leverages extensive human-annotated datasets for task-specific optimization |
Learns dynamically from feedback and rejection sampling, reducing reliance on human-generated labels |
| Reinforcement Learning (RL) |
RLHF (Reinforcement Learning from Human Feedback) |
Applied after SFT to fine-tune alignment and response quality |
RL is the primary training method, evolving the model without supervised pre-training |
| Self-Evolution Mechanism |
Human labelers curate reward models and ranking systems to refine outputs |
Model undergoes self-improvement by dynamically optimizing for accuracy and coherence |
| Reward System |
Accuracy Rewards |
Uses correctness evaluation based on predefined datasets |
Employs reward modeling to reinforce logical consistency and high-confidence responses |
| Format and Readability Rewards |
Encourages human-preferred formats, such as structured dialogue responses |
Uses rewards to ensure syntactical correctness, reasoning depth, and structured outputs |
| Training Stability |
Cold Start vs. Pre-trained Checkpoints |
Models are fine-tuned from fully pre-trained language models |
Starts RL from a base checkpoint, gradually refining outputs |
| Alignment Approach |
Combining Supervised and RL |
Balances SFT and RLHF to maximize model accuracy and coherence |
Achieves alignment primarily via RL, leading to emergent reasoning behavior |
| Model Deployment & Ecosystem |
Customization & Open-Source |
Proprietary with limited open access |
Open-source distribution for broader experimentation and community refinement |
| Scalability & Resource Use |
Requires significant labeled data and computational resources |
More efficient for reasoning tasks, leveraging smaller-scale training while maintaining performance |
I had never paid attention to financial markets before. Numbers flashing on screens, analysts debating in jargon, and stock tickers scrolling endlessly—these were distant, almost mythical symbols to me. But then, I saw the headlines. Rather than discussing the technical breakthroughs of DeepSeek, the reports focused on fears: where the data was stored, whether American security was at risk, and how it was alarmingly cheap to build. The financial world responded not with admiration for the innovation but with concern over control and power. Investors hesitated, unsure whether this disruption signified a technological leap or a geopolitical threat.
"My roommate had a single story of Africa, a single story of catastrophe. In this single story, there was no possibility of Africans being similar to her in any way, no possibility of feelings more complex than pity, no possibility of a connection as human equals." (Adichie, 2009)
This wasn’t just about AI. It was about who gets to tell the story of AI.
What this demonstrates, I think, is how impressionable and vulnerable we are in the face of a story, particularly in the AI field. When one methodology dominates, it narrows our expectations and distorts our sense of what’s possible.
"What if my roommate knew about Nollywood, full of innovative people making films despite great technical odds, films so popular that they really are the best example of Nigerians consuming what they produce? What if my roommate knew about my wonderfully ambitious hair braider who has just started her own business selling hair extensions?" (Adichie, 2009)
As AI confronts challenges of scalability, cost, and accessibility, DeepSeek offers a counter-narrative. Just as Chimamanda Ngozi Adichie warned against reducing cultures to a single story, AI discourse risks oversimplification. Framing AI progress as a race for dominance erases collaboration, ethics, and long-term impact from the conversation."
The danger of a single story is its power to silence alternatives.
When we succumb to the single story, we stop asking the hard questions. Who decides the metrics that matter? Whose voices are amplified, and whose are silenced?
“There is a word, an Igbo word, that I think about whenever I think about the power structures of the world, and it is "nkale." It's a noun that loosely translates to "be greater than another." Like our economic and political worlds, stories too are defined by the principle of nkale. How they are told, who tells them, when they are told, how many stories are told are really dependent on power. Power is the ability not just to tell the story of another person, but to make it the definitive story of that person. The Palestinian poet Mourid Barghouti writes that if you want to dispossess a people, the simplest way to do it is to tell their story.” (Adichie, 2009)
"That is how to create a single story: show a people as one thing, as only one thing, over and over again, and that is what they become. It is impossible to talk about the single story without talking about power." (Adichie, 2009)
Tell the story of evaluating AI feasibility, and you have a tale of rigorous analysis—where ChatGPT o1 is tested for inference-time scaling and precision, while DeepSeek’s RL-driven models are examined for adaptability with minimal labeled data. It becomes a story of choice, of constraints, of what makes up the assembly, and of how AI can serve its intended purpose most effectively.
Tell the story of experimenting with multi-stage reinforcement learning, and you have a story of refinement—of iterative reward shaping, where real user interactions mold alignment beyond static fine-tuning. DeepSeek’s approach demonstrates that learning does not stop at deployment but continues to evolve dynamically.
Tell the story of optimizing Chain-of-Thought, and you have a story of structured reasoning—where adjusting the depth of logical progression and refining prompt engineering lead to greater precision and creativity. Expanding inference-driven CoT transforms AI responses from mere outputs to clear, contextualized insights.
Tell the story of engaging with the research community, and you have a story of collective progress—where forums, conferences, and open-source collaborations push AI beyond proprietary constraints. The field moves forward not by isolated advancements, but by shared knowledge and ongoing discourse.
And you have an entirely different story.
"The American writer Alice Walker wrote this about her Southern relatives who had moved to the North, and she introduced them to a book about the Southern life that they had left behind. They sat around reading the book themselves, listening to me read the book, and a kind of paradise was regained. I would like to end with this thought: that when we reject the single story, when we realize that there is never a single story about any place, we regain a kind of paradise." (Adichie, 2009)
Tell the story of AI as a battleground for narratives, and you have an entirely different story—one where media and financial analysts shape public perception, where regulatory fears overshadow technical breakthroughs, and where the AI community sees milestones in innovation instead of threats. The industry does not view progress like DeepSeek’s as a disruption to be feared but as a necessary step in the evolution of intelligence. Researchers, engineers, and policymakers see this step as an expansion of possibilities, a diversification of methodologies that ensures AI is not locked into a single path.
And you have an entirely different story.
Dedicated to Chimamanda Ngozi Adichie. And the technical specialists.
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