This isn’t a story about brute-force computing or secret algorithms. It’s about reinventing how AI learns: a student-like journey from chaotic scribbles to structured genius – fuelled by trial-and-error curiosity, a coach who fields only the right players, and a playbook so efficient it fits in your pocket.
Imagine teaching a child math without textbooks or lectures. Instead, you let them solve puzzles through trial and error, rewarding correct answers. This is how DeepSeek-R1, an AI model rivalling OpenAI’s top systems, learned to reason – using reinforcement learning (RL). Here’s a breakdown of its training journey, simplified with everyday analogies:
The Trial-and-Error Kid
Method: DeepSeek-R1-Zero used pure reinforcement learning without supervised fine-tuning.
Analogy: A child solves math problems without examples. Every correct answer earns praise and wrong ones get ignored. Over time, the child discovers patterns and refines strategies.
- How it worked: The AI tried thousands of ways to solve math, coding and logic problems. Correct answers earned rewards, teaching it to prioritise effective reasoning paths.
- Result: Achieved 71% accuracy on math exams (AIME 2024) for DeepSeek-R1-Zero, but answers were messy and sometimes mixed languages.
Study Guide Update
Method: Cold-Start Data + Chain-of-Thought (CoT) Templates.
Analogy: Child initially solves math problems with messy scribbles. To improve, child is given a workbook where every solved example shows the steps. The child learns to mimic this structured approach, avoiding shortcuts or skipped steps.
- How it worked: Engineers added 3 000+ curated examples with structured Chain-of-Thought (CoT) reasoning – like step-by-step math calculations and proofs or code debugging steps – where the AI’s thinking process was explicitly separated from the final answer using
<think>tags. - Result: Accuracy jumped to 79.8% (matching OpenAI’s o1 79.2%), and answers became readable.
The Tutor-Student Combo
Method: Hybrid Training (RL + Supervised Learning).
Analogy: The student learns through self-guided practice while consulting a mentor for feedback, refining methods to ensure solutions are both creative and precise.
- How it worked: The AI combined RL exploration with iterative Supervised Fine-Tuning (SFT), generating 800K high-quality samples by filtering RL outputs for clarity / correctness and periodically fine-tuning with human-approved examples (e.g., clear summaries) to align with preferences.
- Result: Balanced creativity and accuracy, excelling in both STEM and writing tasks. Supported by benchmarks (e.g., 97.3% on MATH-500, 87.6% on AlpacaEval 2.0).
The Cheat Sheet Distillation
Method: Knowledge Transfer to Smaller Models
Analogy: A smart student condenses their textbook into a pocket cheat sheet, letting friends solve problems almost as well without the bulky notebook.
- How it worked: Engineers condensed the massive 671B “expert” model’s problem-solving skills into smaller, efficient versions (like Qwen-7B) using 800 000 key examples, letting them run on everyday devices (e.g., a gaming laptop).
- Result: The distilled 7B model achieved 55.5% accuracy on AIME math problems (vs. GPT-4o’s 9.3%) and 37.6% on LiveCodeBench coding tasks (vs. GPT-4o’s 34.2%), proving smaller models can rival giants in specialised areas.
Model Architecture
Architecture: Mixture of Experts (MoE) architecture activates only the specialised “experts” per task instead of the entire model.
Analogy: Like a football coach sending only the striker to score goals, the goalie to block shots, or the midfielder to set up plays – instead of making the entire team run around for every move.
- How it works: Uses 5.5% of total capacity (37B/671B parameters) per query, activating only task-specific experts.
- Result: Unlike dense models like GPT-4 (which use 100% “brainpower” every time), this slashes energy / hardware costs by ~70% while matching performance.
Final thoughts
Like DeepSeek-R1 activating only 5.5% of its mind with task-specific experts, human brain focuses energy on regions like the prefrontal cortex for logic or visual cortex for sight – not lighting up all neurons at once – proving both thrive on precision rather than brute force. DeepSeek-R1’s efficiency makes AI accessible but urges us to address bias, sustainability, and responsible use – core pillars of ethical AI. Learn how to balance innovation with ethics here.
At Teamit we’re already experimenting with DeepSeek-R1’s API and local deployments, exploring its potential to deliver GPT-4-level performance at a fraction of the cost – laying the groundwork for scalable, budget-friendly AI solutions that could transform your business in 2025 and beyond. If this is relevant at your company at the moment, you can reach out to me on LinkedIn or contact Teamit at marko.nissila@teamit.fi to learn more.
