Here’s a simple way to understand pure reinforcement learning with carefully crafted reward functions:

What is Reinforcement Learning (RL)?

RL is a training method where an AI model (often called an “agent”) learns by interacting with an environment. The agent takes some action and then gets feedback in the form of a reward (could be a point system or some signal indicating success or failure). Over many repetitions, the agent adjusts its strategy to maximize its cumulative reward. Why “Pure” RL?

In many AI systems, developers provide large supervised datasets with labeled examples (“here’s the correct answer, learn from it”). “Pure” RL means the agent is not given direct instructions on the “right” next step or the exact path to follow. Instead, it must discover its own strategies only by experimenting and seeing what yields higher rewards. The Role of Carefully Crafted Reward Functions

A reward function is what tells the model when it has done something good or bad. For instance: “+1 point if you solve the math puzzle correctly,” or “-1 point if you produce a contradictory statement.” This design is crucial. If the reward is too vague or too easy to exploit, the model might learn sneaky shortcuts (like simply repeating a phrase that often leads to reward). By crafting a reward function that values step-by-step reasoning, internal consistency, or accurate solutions, you encourage the model to develop more thoughtful behaviors—like verifying intermediate steps and taking more time on complex tasks. How This Leads to Step-by-Step Reasoning

If the reward structure favors correct intermediate reasoning (not just final answers), the model learns to break problems into smaller chunks. It might generate a “chain of thought” (like a series of reasoning steps) and check each step for consistency—because that’s what reliably yields the highest reward in the long run. Over time, the model refines this process until it can handle more and more complex tasks. In short, by relying on RL—where the model explores different strategies and gets feedback—and by carefully defining what earns a reward, DeepSeek’s system encourages the AI to adopt better reasoning habits on its own, without needing massive labeled datasets that show it how to solve every problem.

The beauty of the MOE model approach is that you can decompose the big model into a collection of smaller models that each know different, non-overlapping (at least fully) pieces of knowledge. DeepSeek's innovation here was developing what they call an "auxiliary-loss-free" load balancing strategy that maintains efficient expert utilization without the usual performance degradation that comes from load balancing. Then, depending on the nature of the inference request, you can intelligently route the inference to the "expert" models within that collection of smaller models that are most able to answer that question or solve that task.

Generating text faster is a big deal for real-time applications (think customer service chatbots or interactive assistants) where the user wants quick responses. Predicting multiple tokens also makes it easier to scale: you can serve more users or handle more requests with the same amount of hardware.

High compute 'cost' upfront, for training', marginal compute 'cost' for inference.

Training a large model tends to be a one-time (but very expensive) up-front process. The total GPU (or other specialized hardware) hours required can be massive, driving much of the overall “cost” for producing a new model. Inference (using the model for predictions or generating outputs) is typically far cheaper per request than training. Each inference call just runs forward passes through the model, which is much lighter in computation compared to the many forward and backward passes needed during training. That said, inference costs can add up if a model is deployed at very large scale—especially large language models, which can have high per-token inference costs. But on a per-use basis, inference is still relatively small compared to the up-front compute for training.

Segmentation of the markets leads to opportunity

FLOPS stands for “Floating Point Operations Per Second.” It is a standard measure of a computer’s performance, indicating how many floating-point calculations (additions, multiplications, etc.) the system can handle in one second. High FLOPS ratings generally mean higher potential for quickly running complex numerical computations—particularly important in AI and machine learning tasks.

Nvidia currently holds a dominant position in the market for AI compute hardware—especially GPUs used in training and inference. While it is not literally the only option, the company’s technology and ecosystem have led to a large share of the data center capital spending on AI workloads flowing toward Nvidia products. Its robust software platform (including CUDA and related libraries), longstanding relationships with cloud providers, and high-performance hardware have contributed to this situation, resulting in a market share that can appear “monopolistic.”

Brief Explanation

Technical Lead: Nvidia pioneered specialized GPUs and software tools for AI, making its technology the default choice for many enterprises and research teams. Software Ecosystem: CUDA (Nvidia’s parallel computing platform) is mature, widely adopted, and well-supported, creating high switching costs for developers. Cloud Provider Partnerships: Major cloud providers (AWS, Azure, Google Cloud) heavily feature Nvidia GPUs, which solidifies Nvidia’s presence and channels a large portion of AI-related spending its way. Competition Exists: Competitors like AMD, Intel, and various AI-chip startups do exist, but they currently hold smaller market shares. Growing interest in alternatives may eventually reduce Nvidia’s dominance, but for now, Nvidia’s position remains strong.

Nvidia’s software ecosystem—centered on the CUDA platform and its extensive libraries—offers a few key advantages:

Easy Integration with AI Frameworks: CUDA support is built right into popular AI tools (like TensorFlow and PyTorch), so developers automatically get GPU acceleration without re-writing large sections of code.

Optimized Performance: Nvidia continuously refines its libraries (cuDNN, TensorRT, etc.) to extract maximum speed from its GPUs for both training and inference.

Mature Developer Tools and Documentation: Years of updates and community feedback mean robust toolsets, reliable drivers, and a wealth of tutorials, making the learning curve more manageable.

Rich Ecosystem of Extensions and Plugins: Nvidia has packaged domain-specific SDKs (e.g., Clara for healthcare, Metropolis for smart cities), which simplify development for specialized use cases.

High Switching Costs: Because so many workloads, frameworks, and research projects have been built and tested on CUDA, organizations are often reluctant to switch to other platforms. This entrenches Nvidia’s position and keeps its ecosystem strong.