DeepSeek R1: The AI That Thinks Step by Step

When DeepSeek released their R1 model last month, it captured widespread attention—not just for its impressive benchmark performance but because it represents a fundamental shift in AI reasoning.

Today, I’ll break down what makes DeepSeek R1 special and why it matters for the future of AI tools like those we build at Promptus. 🧠

🔍 Understanding DeepSeek’s Reasoning Revolution

• Transparency & Openness
  DeepSeek has shared their weights, techniques, and methodologies, giving insight into modern reasoning models. This openness reveals how reasoning-focused architectures differ from classic LLMs.
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• Visible, Step-by-Step “Thought” Process
  • Traditional models (e.g., GPT-4o Mini) often provide answers directly from memorized data.
  • DeepSeek’s reasoning models “think through” problems, questioning themselves and progressing methodically—much like a human solving a complex task.
  • This visible chain of thought is transformative for applications requiring deep problem-solving or multi-step logic.
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• Benchmark Shifts
  Reasoning models have rapidly taken top spots in AI benchmarks, signaling that methodical, self-questioning architectures outperform direct-answer approaches on complex tasks.

🤔 The shift from “give me the answer” to “show your reasoning” opens new possibilities for workflows that need nuanced understanding and context-awareness.

⚙️ How DeepSeek Built Their Reasoning Models

1. Early Experiments: DeepSeek Math & V3
   • DeepSeek Math demonstrated that models could learn from their own reasoning attempts.
   • DeepSeek V3 was a capable LLM with a modern architecture, but the real breakthrough emerged next.
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2. R1-Zero: Powerful but Raw
   • R1-Zero exhibited exceptional reasoning ability yet had usability issues (e.g., random language switches, hard-to-read outputs).
   • This highlighted potential but required refinement.
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3. Iterative Fine-Tuning & Self-Generated Data
   • Rather than manual data collection, DeepSeek used R1-Zero’s outputs to programmatically generate ~600,000 high-quality reasoning examples.
   • Through stages of supervised fine-tuning and reinforcement learning, they evolved V3 into a user-friendly R1 that retained strong reasoning.
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4. Efficiency & Scale
   • Generating reasoning examples programmatically saved immense cost and time versus human annotation.
   • Distillation of larger reasoning models into smaller versions proved more effective than direct RL-based training on small models.

💡 Automating reasoning-example generation signals a new paradigm: models improving themselves beyond human-labeled data constraints.

🌟 What DeepSeek R1 Means for AI’s Future

1. Inference Compute Demands
   • Reasoning models produce longer, multi-step outputs (up to 20× length of direct-answer models).
   • Widespread adoption implies huge increases in inference capacity—both a challenge and an opportunity for richer applications.
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2. Breaking Training Plateaus
   • Traditional progress relied on limited human-generated data. Self-improvement via reasoning can push models beyond previous data limits.
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3. Better Small Models via Distillation
   • Distilled reasoning models run efficiently on local hardware (e.g., laptops, desktops) while retaining substantial capability.
   • Aligns with Promptus’s goal: powerful tools accessible on-device, not only in the cloud.
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4. Enhanced Creative Workflows
   • For Promptus, stronger reasoning means workflows that understand complex creative intentions—e.g., refining design prompts, suggesting coherent multi-step transformations, or troubleshooting pipeline issues.
   • Distilled R1 variants could run in Promptus App, offering offline reasoning support.

🚀 DeepSeek’s approach accelerates innovation industry-wide. We expect similar patterns in future models from major labs, amplifying capabilities available to creators.

🔧 Taking Advantage of These Innovations in Promptus

• Integrating Reasoning into Cosyflows
  Imagine visual workflows that not only generate images/videos but also “think through” design challenges:
  • Suggesting composition adjustments
  • Ensuring narrative coherence across scenes
  • Offering iterative feedback loops inside the workflow
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• Model Multi-Modality (MoMM) + Reasoning
  Combining DeepSeek’s reasoning with image/video models (Stable Diffusion, Veo 3, etc.) could unlock entirely new creative possibilities:
  • Automated storyboard generation with contextual guidance
  • Intelligent asset selection based on project goals
  • Enhanced troubleshooting of complex pipelines
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• On-Device & Cloud Hybrid
  • Use distilled R1 locally for quick reasoning tasks (e.g., prompt refinement, project planning).
  • Offload heavier operations to cloud when needed, maintaining seamless user experience without exposing complexity.

✨ By leveraging DeepSeek R1’s open methodologies, Promptus can evolve workflows to be more intuitive, powerful, and accessible to creators of all skill levels.

🔮 Looking Ahead

The DeepSeek R1 breakthrough reminds us we’re still early in AI’s evolution. Each advancement unlocks new doors for creativity and problem-solving. At Promptus, we’re committed to harnessing these innovations—embedding reasoning-aware capabilities into our platform so that both professionals and newcomers can benefit:

• Accessible Reasoning: Integrate step-by-step logic into visual workflows, helping users refine ideas and achieve coherent results.
• Efficient Local Models: Run distilled reasoning models in Promptus App for privacy, offline access, and lower latency.
• Continuous Innovation: Stay aligned with emerging research, rapidly incorporating new reasoning techniques to enrich creative tools.

If you’d like to experience these advancements firsthand, sign up at promptus.ai and choose between Promptus Web (browser-based) or Promptus App (desktop). Together, we’ll shape AI creation that’s more intuitive, powerful, and broadly accessible than ever before.

The DeepSeek R1 revolution is just the beginning. Let’s build the future of AI-enhanced creativity—one reasoning step at a time.