It’s been a while since I have read such a detailed technical paper. Kudos to the Deepseek team for the level of information that they have provided in the paper (attached).

Even after spending more that 2 days on this paper, I feel as though I have barely got 50% of the details.

Here is summary of what I could make out on Deepseek’s release that has shaken (or should I say woken up) the entire AI community.

The question that most of us have is “How did the model meet this level of performance at a low training cost” ? The answer lies in their architectural ingenuity that is nothing short of fascinating. Let me try to make it as simple as possible for you:

1️⃣ Reducing Costs While Maintaining Performance: They achieve this with several clever techniques
👉 Selective Attention with Mixture-of-Experts (MoE): Instead of running the full model every time, only specific “experts” are activated based on the query. This reduces computational load while maintaining precision.
👉 Smarter Precision (FP8): FP8 precision simplifies calculations without compromising model accuracy, cutting hardware costs.
👉 Pipeline Overlap with DualPipe Algorithm: By overlapping data loading, processing, and model training, idle time is eliminated, accelerating the process.
👉 Knowledge Distillation: Instead of training a huge model from scratch, smaller versions are trained using insights from a larger, well-trained model.
👉 Memory Optimization: Efficient algorithms reduce memory usage without sacrificing performance, enabling the model to scale at a fraction of the cost.

2️⃣ The Reinforcement Learning Breakthrough:
One of the standout innovations is its Group Relative Policy Optimization (GRPO). Here’s how it works:
👉 GRPO improves the model’s responses by comparing them within a group of generated answers. It uses this group to “teach itself” what works best—without relying on an external critic.
👉 For each question, the model generates a group of answers.
👉 It compares these answers to determine which ones are better, using a measure called advantage (A) -> (rewards minus group average)
👉 Then, it adjusts the new model’s probabilities to favor better answers while discouraging worse ones.

🎯  In Simple Terms: The model says to itself, “If this answer is better than average, do more of that. If it’s worse, avoid it—but don’t change too much at once.”

GRPO improves the model by self-comparing outputs, enabling faster learning, simpler training, and significant cost reduction.

This is the beauty of competition—when the goalposts keep moving, innovation thrives. DeepSeek-V3’s breakthrough will push others to return to the drawing board, creating cost-efficient models with even greater performance.