Reinforcement Learning Infrastructure: GPU Clusters for RLHF and Robotics

Updated December 11, 2025

December 2025 Update: RLHF training spending 80% of compute on sample generation—throughput optimization critical. OpenRLHF enabling 70B+ parameter RLHF via Ray-based model separation across GPUs. NVIDIA three-computer architecture: DGX for training, Omniverse for simulation, Jetson Thor for on-robot inference. vLLM acceleration dramatically improving sample generation throughput.

RLHF training spends 80% of compute time on sample generation, making throughput optimization the critical infrastructure challenge for organizations aligning large language models with human preferences.¹ OpenRLHF emerged as the first high-performance open-source framework enabling 70B+ parameter RLHF training by separating Actor, Reward, Reference, and Critic models across different GPUs.² Meanwhile, NVIDIA's three-computer architecture for physical AI connects DGX supercomputers for training, Omniverse servers for simulation, and Jetson AGX Thor for on-robot inference.³ Reinforcement learning workloads demand infrastructure patterns distinct from standard supervised training, and organizations building RL capabilities need architecture decisions that account for these differences.

The infrastructure divergence starts with memory requirements. Existing RLHF frameworks struggle with the immense memory demands of 70B+ parameter models, limiting the full potential of alignment techniques.⁴ Excessive model partitioning across GPUs leads to memory fragmentation on individual devices, reducing effective batch sizes and slowing overall training. Robotics simulation adds another dimension: training hundreds or thousands of robot instances in parallel requires GPU-accelerated physics engines running alongside neural network training.⁵

RLHF infrastructure patterns

Reinforcement learning from human feedback involves orchestrating multiple distinct phases that impose different infrastructure requirements. Reward modeling trains a model to predict human preferences. The RL phase then uses the reward model to guide policy optimization. Both phases involve large model inference and training simultaneously, creating resource contention patterns absent in standard supervised learning.

Multi-model orchestration

RLHF training requires running four models concurrently: the Actor (policy model being trained), the Reward model (scoring responses), the Reference model (preventing distribution drift), and the Critic model (estimating value functions).⁶ Each model may reach tens of billions of parameters. Managing memory allocation and compute scheduling across four 70B models exceeds typical training infrastructure complexity.

OpenRLHF addresses multi-model challenges through Ray, a distributed task scheduler that intelligently allocates models across GPUs without excessive partitioning.⁷ The framework leverages Hybrid Engine scheduling, allowing all models and vLLM inference engines to share GPU resources. The approach minimizes idle time and maximizes utilization by dynamically rebalancing resources as workload demands shift between training and inference phases.

Sample generation bottleneck

The 80% compute time spent on sample generation reflects a fundamental RLHF characteristic: policy models must generate complete responses before reward scoring can occur.⁸ Standard training batches static data through forward and backward passes. RLHF generates novel samples at each step, creating inference bottlenecks that dominate wall-clock time.

vLLM acceleration dramatically improves sample generation throughput through optimized memory management and parallel processing across multiple GPUs.⁹ Auto Tensor Parallelism (AutoTP) in OpenRLHF automatically distributes inference across available GPUs, achieving high-throughput generation that keeps training phases fed with fresh samples.

Systems-level optimizations (2025)

Research teams developed multiple approaches to improve RLHF throughput during 2024 and 2025. RLHFuse, AReal, and Verl improve throughput via fine-grained parallelism, collocating models to reduce communication overhead and dynamically scaling GPU resources to match workload demand.¹⁰

Verl, RLHFuse, ReaL, and PUZZLE colocate LLMs of different stages in the same resource pool, improving GPU utilization when individual models would leave resources idle.¹¹ StreamRL disaggregates training and generation stages, running them asynchronously in a pipeline that exploits the high memory-bandwidth advantages of dedicated inference clusters.

OPPO (Pipeline Overlap for PPO) achieves additional speedups by overlapping computation phases that previously ran sequentially.¹² The technique reduces idle time by starting subsequent batches before prior batches complete, trading slightly increased memory usage for improved throughput.

Physical AI and robotics infrastructure

Robotics applications introduce simulation requirements alongside neural network training. Robots must learn in simulated environments before real-world deployment, requiring physics-accurate virtual worlds running at speeds that make reinforcement learning practical.

NVIDIA's three-computer architecture

NVIDIA designed a comprehensive stack for physical AI development spanning training, simulation, and deployment.¹³ DGX AI supercomputers handle model training with the compute density required for large-scale RL. Omniverse and Cosmos running on RTX PRO Servers provide simulation environments where robots train in physics-based digital twins. Jetson AGX Thor handles on-robot inference with real-time performance for autonomous operation.

The architecture reflects physical AI's unique demands. Robots must process sensor data, reason about environment state, plan actions, and execute movements within milliseconds.¹⁴ Training infrastructure must produce models that meet these latency constraints when deployed on edge hardware with limited compute budgets.

GPU-accelerated simulation

NVIDIA Isaac Lab provides an open-source framework for robot training built on Isaac Sim, supporting reinforcement learning, learning from demonstrations, and motion planning workflows.¹⁵ The framework enables training hundreds or thousands of robot instances in parallel, iterating policies faster than real-world training could ever achieve.

Newton, a GPU-accelerated physics engine co-developed by Google DeepMind and Disney Research, provides high-speed, physically accurate, differentiable simulation.¹⁶ Differentiable physics enables gradient-based optimization through simulation, accelerating policy learning compared to black-box reinforcement learning approaches.

The sim-first approach proves essential for physical AI development. Developers validate robot behaviors in digital twins before deployment, catching failures that would damage physical hardware or harm humans.¹⁷ The methodology requires simulation infrastructure capable of running physics at faster-than-real-time speeds while maintaining accuracy sufficient for policy transfer to real robots.

Multi-GPU orchestration for robotics

NVIDIA OSMO provides cloud-native orchestration for complex robotics workloads spanning multiple stages and containers across multi-GPU and multi-node systems.¹⁸ Robotics development pipelines involve data collection, model training, simulation testing, and deployment packaging. Coordinating these stages across heterogeneous GPU resources requires orchestration beyond standard Kubernetes capabilities.

Leading robotics companies including Agility Robotics, Boston Dynamics, Figure AI, and Skild AI adopt NVIDIA Isaac and Omniverse technologies.¹⁹ Research institutions at Stanford, ETH Zurich, and National University of Singapore leverage the same accelerated computing infrastructure for advancing robotics research.

Infrastructure requirements comparison

RLHF and robotics RL share some infrastructure patterns but diverge significantly in others.

Memory requirements

RLHF for LLM alignment requires hosting multiple large models simultaneously. A 70B Actor, 70B Reference, and separate Reward and Critic models may require 8-16 H100 GPUs just for model weights before accounting for optimizer states and activations.²⁰ Robotics policies typically involve smaller models but require concurrent simulation state.

Robotics simulation memory scales with environment complexity and parallel instance count. Running 1,000 simulated robots with physics state, sensor data, and neural network inference consumes substantial GPU memory even with relatively small policy networks.

Compute patterns

RLHF workloads alternate between inference-heavy sample generation and training-heavy policy updates. Infrastructure must handle both patterns efficiently, either through shared resources with dynamic scheduling or dedicated pools for each phase.

Robotics training runs simulation and policy updates concurrently. Physics computation overlaps with neural network forward and backward passes. GPU utilization patterns differ from language model training, with more consistent load rather than the bursty inference of RLHF sample generation.

Networking requirements

Multi-node RLHF training requires high-bandwidth interconnects for gradient synchronization and model state sharing. The four-model architecture multiplies communication overhead compared to single-model training.

Robotics distributed training may involve additional communication for shared environment state when multiple policies interact in the same simulation. Centralized critics or shared world models require gathering observations from parallel simulation instances.

Deployment at scale

Organizations deploying RL infrastructure at scale face decisions about cluster architecture, resource allocation, and operational practices.

Cluster design considerations

RL workloads benefit from homogeneous GPU clusters that simplify scheduling and avoid performance variations from mixed hardware. Memory-optimized configurations prove valuable for RLHF's multi-model requirements, while compute-optimized configurations suit robotics simulation.

Networking investment matters more for RL than typical inference workloads. NVLink interconnects within nodes accelerate the model-parallel communication that RLHF requires. InfiniBand or high-speed Ethernet enables multi-node scaling as model sizes exceed single-node capacity.

Professional infrastructure deployment

Reinforcement learning infrastructure complexity exceeds typical AI deployment requirements. The multi-model coordination, simulation integration, and specialized networking create integration challenges that require experienced teams to resolve efficiently.

Introl's network of 550 field engineers specialize in GPU infrastructure deployments supporting advanced AI workloads including reinforcement learning systems.²¹ The company ranked #14 on the 2025 Inc. 5000 with 9,594% three-year growth, reflecting enterprise demand for professional infrastructure services.²² Organizations building RL capabilities benefit from deployment expertise that accelerates time to operational infrastructure.

Managing GPU deployments across 257 global locations enables organizations to place RL infrastructure where researchers and applications reside.²³ Introl handles deployments reaching 100,000 GPUs with over 40,000 miles of fiber optic network infrastructure, providing scale matching the largest RL initiatives.²⁴

Physical infrastructure quality directly impacts RL training stability. Thermal throttling, power fluctuations, and network inconsistencies manifest as training instabilities that complicate debugging. Professional deployment ensures the infrastructure foundation supports reliable RL experimentation.

The RL infrastructure trajectory

Reinforcement learning infrastructure demands will intensify as organizations pursue more ambitious alignment goals and robotics applications. GPT-4's training reportedly used extensive RLHF, and subsequent models expand RL usage further.²⁵ Physical AI deployments from humanoid robots to autonomous factories require simulation and training infrastructure at unprecedented scale.

The frameworks and hardware continue evolving rapidly. OpenRLHF, DeepSpeed-Chat, and other open-source projects lower barriers to RLHF experimentation. NVIDIA's Isaac ecosystem provides increasingly integrated robotics development tools. Organizations investing in RL infrastructure today position themselves for the alignment and embodied AI advances that RL enables.

The infrastructure patterns that work for supervised learning do not automatically transfer to reinforcement learning. Multi-model orchestration, simulation integration, and specialized communication patterns require deliberate architectural decisions. Organizations that understand RL's distinct infrastructure requirements build systems that accelerate rather than constrain their AI ambitions.

References

Key takeaways

For strategic planners: - OpenRLHF enables 70B+ parameter RLHF training; GPT-4 reportedly used extensive RLHF; RL usage expands in subsequent models - NVIDIA three-computer architecture for physical AI: DGX (training), Omniverse (simulation), Jetson AGX Thor (robot inference) - Leading robotics companies (Agility, Boston Dynamics, Figure AI, Skild AI) adopt NVIDIA Isaac and Omniverse technologies

For ML engineers: - RLHF spends 80% of compute time on sample generation—throughput optimization is the critical infrastructure challenge - Four models run concurrently: Actor, Reward, Reference, Critic (each potentially 70B parameters); vLLM acceleration critical - RLHFuse, Verl, StreamRL improve throughput via fine-grained parallelism and model collocation; OPPO overlaps computation phases

For infrastructure architects: - 70B Actor + 70B Reference + Reward + Critic = 8-16 H100 GPUs just for model weights before optimizer states - OpenRLHF leverages Ray for distributed scheduling with Hybrid Engine; models share GPU resources dynamically - NVLink within nodes accelerates model-parallel communication; InfiniBand enables multi-node scaling beyond single-node capacity

For robotics teams: - Isaac Lab provides open-source robot training framework; Newton (Google DeepMind + Disney Research) provides GPU-accelerated differentiable physics - OSMO provides cloud-native orchestration for multi-stage robotics pipelines across multi-GPU/multi-node systems - Sim-first validation catches failures before physical deployment; simulation must run faster-than-real-time while maintaining transfer accuracy

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