What is Reinforcement Learning (RL)?

We’re thrilled to announce the general availability of NVIDIA’s cutting-edge H200 Tensor Core GPUs across our global platform. The NVIDIA H200 GPU supercharges AI workloads with game-changing performance and memory capabilities, and now you can leverage this powerhouse on Ori’s end-to-end AI cloud.

Realize a new level of performance for your AI

Built on the advanced Hopper architecture, the H200 delivers leaps in throughput for both AI and HPC workloads. It boosts AI inference speed by up to 2x versus the H100 on large language models, enabling faster responses and higher user capacity. This means quicker training runs, snappier AI inference, and accelerated time-to-market.

Larger and faster memory for higher efficiency

An important hurdle to advancing AI progress is the memory wall. Model attributes such as accuracy, sequence length and latency are either directly or indirectly influenced by memory bandwidth and memory capacity of GPUs. Ample and fast memory is an essential requirement to realize the full computational benefits of a high performance GPU architecture such as Hopper.

Each H200 GPU comes equipped with 141 GB of HBM3e memory running at 4.8 TB/s. That’s 76% more memory capacity and 43% higher memory bandwidth than the H100 GPU. This enormous, fast memory pool allows ML developers to fit larger models and datasets into a single GPU, reducing the need for model sharding. It also improves latency for inference, letting models fully exploit Hopper’s compute advances. In practical terms, many models that wouldn’t fit in a single H100 GPU can now run on an H200 GPU, helping ML teams build AI more efficiently.

What can you do with the NVIDIA H200 GPU?

Train & finetune large models: The faster and expanded NVIDIA H200 memory enables improved training and inference for state-of-the-art (SOTA) models. Whether you are building foundation models or training compute-intensive models such as image and video generation, H200 GPUs are a great choice for models that are trained on vast amounts of data.

Run inference on 100+ billion parameter models with ease: The enhanced HBM3E memory capabilities of the H200 GPU makes it easier to run inference with much longer input and output sequences with tens of thousands of tokens.

Models with large parameter count now need lesser GPUs. For example, DeepSeek R1 671B which needs about two nodes (consisting of 8 H100 GPUs each) for inference can run on a single H100 node. That means you can serve large models at scale with more efficiently with H200.

Power high-precision HPC workloads: Whether it is scientific models, simulations or research projects, increased memory capacity helps to run models with higher precision formats such as FP32 and FP64 for maximum accuracy, and higher memory bandwidth reduces computing bottlenecks.

Deploy Enterprise AI with greater efficiency: Enterprise AI apps typically run on large GPU clusters, the H200 GPU makes it easy to manage infrastructure with fewer GPUs, greater utilization and enhanced throughput for better ROI.

Why Choose Ori for Your H200 GPU Needs?

Ori Global Cloud isn’t just offering the latest NVIDIA hardware – we provide an entire ecosystem to maximize its value for you. Here are some compelling reasons developers and businesses choose Ori for GPU-intensive workloads:

• End-to-End Platform: Ori accelerates the entire AI pipeline. From running experiments with GPU Instances to training large models with GPU clusters, scaling workloads with Serverless Kubernetes, serving models with Inference Endpoints or building Enterprise AI with a Private Cloud, you can do it all on a single platform. No more switching environments or waiting for access, get your AI models and applications to market faster with Ori.
  
  
• Flexible, Cost-Efficient Usage: We offer flexible pricing models ranging from on-demand pricing to reserved instances and Private Cloud-as-a-Service. Ori provides you H200 GPUs at a fraction of the prices from hyperscale clouds with no surprise fees.
  
  
• Seamless Deployment: Ori is designed for ease of use and scalability as demand for your models and apps grows. You can spin up H200-powered virtual machines via our cloud console, API or CLI with all the necessary NVIDIA drivers and ML frameworks ready to go. If you prefer Kubernetes for your AI workloads, Our Serverless Kubernetes service allows you to scale automatically without the hassles of managing infrastructure.
  
  
• Reliability and Expert Support: When you run on Ori, you’re leveraging a platform engineered for demanding AI workloads. Our environments are monitored 24/7 and built on enterprise-grade hardware and networking. Ori’s team has deep expertise in AI infrastructure, we’ve worked on deployments of large language models and GPU clusters at scale. 

Get Started with NVIDIA H200 on Ori

Ori Global Cloud provides flexible infrastructure for any team, model, and scale. Backed by top-tier GPUs such as the NVIDIA H200, performant storage, and AI-ready networking, Ori enables growing AI businesses and enterprises to deploy their AI models and applications in a variety of ways:

• Leverage GPU Instances as on-demand virtual machines.
• Deploy Private Clouds for flexible and secure enterprise AI.
• Operate Inference Endpoints effortlessly at any scale.
• Scale GPU Clusters for training and inference.
• Manage AI workloads on Serverless Kubernetes without infrastructure overhead.

Ready to accelerate your AI workloads with NVIDIA H200 GPUs?

Advantages of Reinforcement Learning

Reinforcement learning is distinct from the other main machine learning paradigms, and this gives it certain unique strengths. In supervised learning, for example, models learn from a dataset of example inputs paired with correct outputs provided by human designers. By contrast, a reinforcement learning agent is not given explicit “correct” actions for each situation; it must discover successful behaviors on its own through feedback. This makes RL suitable for problems where the correct solution is not obvious or examples of optimal behavior are difficult to provide in advance.

RL also differs from unsupervised learning, which involves finding patterns in data without any external feedback or specific goal. RL is explicitly goal-oriented: it is driven by a reward signal that defines what the agent should achieve. In other words, an RL agent is always optimizing toward a particular objective, rather than merely uncovering data structure.

These differences lead to several key advantages of reinforcement learning in machine learning:

First, an RL agent learns by interacting with its environment, which means it can adapt to dynamic or changing conditions in real time.

Second, because it does not require labeled examples of correct behavior, RL can be applied in domains where such supervised data is scarce or impossible to obtain.

Third, reinforcement learning naturally considers long-term results: it can optimize sequences of decisions to maximize cumulative rewards, whereas other methods might focus only on immediate outcomes.

Finally, through its exploratory trial-and-error process, RL can discover creative or unexpected strategies to achieve its goals – strategies that human designers might not have anticipated.

Real-World RL Applications

Reinforcement learning is often showcased in games and robotics, but its reach extends far beyond those popular examples. In recent years, a variety of less commonly cited domains have begun to benefit from RL techniques:

• Natural Language Processing: RL can be used to fine-tune large language models (LLMs) so that they produce outputs more closely aligned with specific objectives, such as helpfulness or factual accuracy. By treating each response as an “action” and providing a reward signal (for example, through human feedback), the model gradually learns to favor responses that better match the desired quality or style. DeepSeek R1 is an example of Reinforcement Learning that helps meet accuracy expectations by iteratively refining responses with accuracy and format rewards.
• Healthcare: Medical decision-making often involves sequential choices – for example, adjusting a treatment plan as a patient’s condition changes. Researchers have experimented with RL to propose treatment strategies or drug dosing schedules that maximize a patient’s long-term health outcomes. Here the patient’s health status acts as the environment, and each treatment decision is an action taken by the agent.
• Energy Management: RL can improve the efficiency of controlling power systems and smart buildings. An agent might learn to adjust heating and cooling in a large building or data center, keeping conditions comfortable while minimizing energy use. For example, Meta has leveraged reinforcement learning where an agent learns from a physics based simulator to optimize data center cooling. In power grids, RL techniques are being explored to help balance supply and demand or to control battery storage, adapting in real time to fluctuations in usage and renewable energy generation.
• Transportation: RL is being applied to optimize traffic flows in cities. For example, an agent can learn to adjust the timing of traffic lights based on real-time conditions, reducing congestion more effectively than fixed timing schedules.
• Personalization: Online services use RL to tailor content and recommendations to individual users. Rather than relying solely on static historical data, an RL agent treats each user interaction as part of an ongoing environment. It can decide which article, song, or product to present next, learning to maximize the user’s long-term engagement or satisfaction (not just immediate clicks or views).

Challenges in Reinforcement Learning

Despite its promise, reinforcement learning comes with subtle challenges and pitfalls. Some of the lesser-known issues include:

• Reward Hacking: This occurs when an agent exploits flaws in the reward function to achieve high rewards without fulfilling the intended goal. For example, a cleaning robot that is rewarded for removing trash might simply hide the trash out of sight to get the reward, instead of truly cleaning. Such behavior highlights the importance of designing reward signals that truly reflect the desired outcome; otherwise, the agent may satisfy the letter of the goal while undermining its spirit.
• Sample Inefficiency: RL algorithms typically require an enormous number of interactions to learn effectively. An agent may need to experience thousands or even millions of trial-and-error steps before it performs well. This reliance on vast amounts of experience is a major drawback, especially in real-world scenarios where each action (like a robot’s movement or a medical decision) can be slow, expensive, or risky.
• Exploration vs. Exploitation: A fundamental challenge in RL is deciding how much to explore new actions versus exploit known rewarding actions. Too much exploration can waste time on unproductive behavior, while too little can cause the agent to miss out on better strategies. Striking the right balance is difficult, and many algorithms include mechanisms to encourage sufficient exploration. This trade-off remains an active area of research.
• Training Instability: The learning process in RL can be unstable and hard to predict. Since an agent is learning from data it generates itself, small changes in the agent’s behavior can alter the data it sees, sometimes causing feedback loops that destabilize training. It is not uncommon for an agent’s performance to improve for a while and then suddenly collapse due to such effects or due to sensitive tuning parameters. Ensuring stable convergence often requires careful algorithm design and extensive tuning, and even then, reproducing results can be challenging.

Future of Reinforcement Learning

The potential of reinforcement learning is big, but it’s not without limitations. On the one hand, RL could become a cornerstone of increasingly intelligent and autonomous systems. As computational power grows and algorithms improve, RL agents may tackle ever more complex tasks. We can imagine future agents managing the energy usage of entire smart cities, or aiding scientific research by autonomously controlling experiments.

On the other hand, significant challenges must be overcome for this potential to be realized. One major limitation is the need for far greater efficiency and safety in learning. Today’s RL algorithms often require impractically large amounts of trial-and-error experience, which is not feasible in many real-world settings. To address this, researchers are exploring model-based methods (where the agent learns a predictive model of the environment to help it plan ahead) and offline reinforcement learning (learning from pre-collected datasets rather than active trial-and-error) to improve sample efficiency.

Another persistent issue is how to specify the right objectives. A poorly designed reward can lead an agent to behaviors that technically achieve a high score but violate the true intent (as seen in reward hacking). Moreover, agents must learn safely and stay aligned with human values, especially in high-stakes areas.

Active research is opening up new directions to meet these challenges. One promising direction is to integrate reinforcement learning with other machine learning techniques. For example, combining RL with imitation learning (learning from human demonstrations) or using unsupervised pre-training for better state representations can give agents a head start and reduce the burden of pure trial-and-error.

Another avenue is hierarchical reinforcement learning, which breaks complex goals into sub-tasks so that agents can learn and plan at multiple levels. There is also growing interest in more advanced paradigms such as multi-agent reinforcement learning, where multiple agents learn and interact together, and new application domains beyond games or robotics.

Notably, RL techniques have started to aid in training large AI systems for language and dialogue, where an agent must decide on sequences of words or actions. The success of systems like DeepSeek hints that RL could help teach AI to make better decisions even in these complex, abstract domains.

Reinforcement learning has established itself as a core area of AI, defined by its learn-through-interaction approach. Its trial-and-error paradigm has led to impressive achievements and continues to improve. At the same time, recognizing its limitations is driving intensive research to make it more efficient, robust, and safe. With continued innovation and careful application, reinforcement learning is likely to become an increasingly standard tool for building adaptive decision-making systems across many domains.

Chart your own AI reality with Ori

Ori Global Cloud provides flexible infrastructure for any team, model, and scale. Backed by top-tier GPUs, performant storage, and AI-ready networking, Ori enables growing AI businesses and enterprises to deploy their AI models and applications in a variety of ways:

• Private Cloud provides flexible and secure infrastructure for enterprise AI, with private AI workspaces for each of your teams.
• GPU instances, on-demand virtual machines backed by top-tier GPUs to run AI workloads.
• Inference Endpoints to run and scale your favorite open source models with just one click.
• GPU Clusters to train and serve your most ambitious AI models.
• Serverless Kubernetes helps you run inference at scale without having to manage infrastructure.