If GPT-4 was the apex of pretraining, DeepSeek might be the blueprint for what comes next.

Released in two families—DeepSeek-V3 and DeepSeek-R1—this Chinese open-source model series isn’t just catching up to frontier LLMs. It’s reshaping the paradigm entirely. By sidestepping traditional supervised fine-tuning in favor of reinforcement learning (RL), and coupling it with memory-efficient innovations like Multi-head Latent Attention (MLA) and cost-efficient training techniques like FP8 mixed precision and fine-grained MoE, DeepSeek models demonstrate how strategic architectural bets can outpace brute-force scale.

The New Stack: Not Just Bigger, but Smarter

DeepSeek’s success is built on four algorithmic pillars:

Algorithm What It Solves Why It Matters
MLA (Multi-head Latent Attention) KV cache bloat during inference Enables long-context reasoning without exploding memory costs
DeepSeekMoE Inefficiencies in expert allocation Reduces redundancy while enabling massive scale with fewer active parameters
MTP (Multi-Token Prediction) Slow training due to token-by-token prediction Boosts training speed by predicting multiple tokens while preserving causal order
GRPO (Group Relative Policy Optimization) High cost and instability of PPO Eliminates the value model, uses group-normalized rewards, and simplifies RL training

Instead of pretraining a massive model and relying on costly supervised datasets for alignment, DeepSeek-R1-Zero pushes a bold idea: train reasoning skills from scratch using pure reinforcement learning.

This idea—radical even by open-weight standards—paid off. R1-Zero not only develops non-trivial reasoning capabilities but also exhibits emergent behaviors like increasing planning depth when needed. When supervised fine-tuning is added later in R1, the model achieves both high readability and performance comparable to OpenAI’s GPT-o1 on math and code tasks.

Scaling the Right Way

DeepSeek’s engineering story may be its most underappreciated breakthrough:

  • FP8 Training: By using FP8 with fine-grained quantization and high-precision accumulation, DeepSeek-V3 achieves 2.7× training speedups with negligible accuracy trade-off.

  • DualPipe Parallelism: DeepSeek’s novel pipeline parallelism overlaps forward and backward passes to eliminate idle time.

  • FlashMLA Inference: On Hopper GPUs, DeepSeek’s FlashMLA kernel reaches 3000 GB/s memory bandwidth and 580 TFLOPS compute—breaking inference bottlenecks.

  • MoE Load Balancing: DeepSeek avoids overloading a few experts by introducing bias-adjusted dynamic routing and sharing expert parameters across tokens.

These are not just hardware tricks—they represent a philosophy of resource-aware scalability, where speed and capability grow together.

From Open Source to Open Standards

DeepSeek’s full open-weight release under the MIT license unlocks something deeper than access: platform-level freedom. Developers can modify, deploy, or commercialize the models with no usage restrictions. It’s a direct challenge to closed LLM ecosystems—one that’s already had real impact, including helping DeepSeek’s chatbot briefly surpass ChatGPT in App Store downloads.

But beyond the numbers, DeepSeek is quietly redefining what the LLM stack should look like. MoE efficiency, reasoning-focused RL, and aggressive KV-cache optimizations are becoming the new normal.

The Post-Pretraining Loop: RL as the New Core

Perhaps most provocatively, DeepSeek-R1 shows how synthetic data, inference-time feedback, and reinforcement learning can form a self-improving closed loop:

  1. Cold-start RL (R1-Zero) →
  2. SFT from distilled reasoning traces (R1) →
  3. More RL with structured rewards →
  4. Distillation into lightweight models (R1-Distill)

This pipeline doesn’t rely on massive preexisting datasets. It builds its own reasoning corpus by simulating thought, verifying output, and iteratively optimizing policies. That’s a profound departure from the dataset-centric LLM era, and a glimpse at post-pretraining intelligence engineering.

Remaining Questions

Despite its advances, DeepSeek doesn’t solve every problem:

  • Long chain-of-thoughts still come with high latency and memory costs.
  • Reward hacking and jailbreak vulnerabilities remain real threats.
  • Balancing alignment safety and model performance is an open research front.

But these are signs of being on the frontier—not falling behind it.

Final Thought

DeepSeek doesn’t just copy the LLM greats—it reroutes the highway. Its impact lies in reminding us that scale, while still powerful, must now answer to architecture, efficiency, and feedback-driven learning.

The post-pretraining era isn’t about bigger models.

It’s about smarter loops.

Cognaptus: Automate the Present, Incubate the Future.