Best Local LLMs for Every RTX 50-Series GPU (2026)

NVIDIA open-sourced ProRL Agent — an infrastructure framework that separates AI agent rollout execution from RL training. Instead of tightly coupling trajectory generation with the training loop (the standard approach), ProRL Agent serves rollouts through an API. Training code calls the API, gets trajectories back, and updates the model.

The result: an 8B model trained with ProRL Agent nearly doubles its SWE-bench Verified score compared to baselines. The framework is open-source and integrated into NVIDIA NeMo Gym.

The Problem ProRL Agent Solves

Training AI agents with reinforcement learning requires generating thousands of multi-turn interaction trajectories. The agent takes actions, observes results, takes more actions — repeat for dozens of turns per episode, across thousands of episodes.

Existing frameworks (SkyRL-Agent, VeRL-Tool, Agent Lightning) couple this trajectory generation directly to the training loop. This creates three problems:

1. Resource conflicts — rollout execution is I/O-heavy (spawning containers, running code, waiting for tool responses). Training is GPU-heavy. Tying them together wastes both.

2. Portability — switching from one RL trainer to another means rewriting the rollout infrastructure.

3. HPC friction — most rollout systems require Docker with root access. Shared compute clusters (Slurm-managed HPC) typically don't allow this.

ProRL Agent fixes all three by making rollout execution a standalone service.

Architecture

ProRL Agent has three components:

1. Sandbox Environments

Each agent task runs in an isolated sandbox. The AgentHandler interface defines three methods:

• init() — set up the environment (clone a repo, start a server, load test data)
• run() — execute the multi-turn agent loop (LLM generates actions, sandbox executes them)
• eval() — score the result (did the agent solve the task?)

Sandboxes use SingularityRuntime — a rootless container system that works on shared HPC clusters without privileged access. No Docker daemon required.

Performance optimizations include direct pseudo-terminal bash execution and in-process IPython kernels, which cut per-action latency.

2. ProRL Agent Server

An HTTP service that orchestrates rollouts through a three-stage pipeline:

Independent worker pools for each stage prevent bottlenecks. If INIT is slow for one task type, it doesn't block RUN for another.

The server manages LLM backends with min-heap load balancing across multiple inference servers. It supports hot-swapping model checkpoints during training without interrupting in-flight rollouts.

A critical detail: token-in/token-out — the server preserves original token IDs throughout the pipeline. This eliminates re-tokenization drift, where converting between text and tokens introduces subtle training artifacts.

3. RL Trainer Integration

The trainer communicates with ProRL Agent entirely over HTTP. Submit rollout jobs, receive completed trajectories with rewards. The trainer never manages containers, sandboxes, or tool execution.

This means you can swap trainers (VeRL, NeMo RL, or anything else that speaks HTTP) without changing your rollout setup.

Benchmarks

SWE-bench Verified (Software Engineering)

The 8B model nearly doubles its score. This is significant — SWE-bench Verified measures real software engineering ability (reading code, writing patches, passing tests), not synthetic benchmarks.

Cross-Domain Results

Scaling

Throughput increases near-linearly with additional compute nodes. Adding more machines to the rollout cluster directly translates to more trajectories per hour, with no architectural bottleneck.

How It Compares

ProRL Agent is the only framework that fully separates rollout from training and runs without root access. For teams on shared compute infrastructure, this is often the deciding factor.

Connection to BroRL and NeMo Gym

ProRL Agent is part of a broader NVIDIA effort around RL for language models:

• ProRL (original) — extended training duration with 3,000+ steps, N=16 rollouts per prompt
• BroRL — scales in a different dimension: increases rollouts to N=512 instead of more steps. A BroRL-trained 1.5B model scored 63.66 on math benchmarks (vs. ProRL's 62.02) in 98 hours instead of 134
• NeMo Gym — the unified library that ties these tools together. ProRL Agent handles the infrastructure, NeMo Gym provides the training environments

The BroRL checkpoint (1.5B Qwen-based) is available on HuggingFace: nvidia/Nemotron-Research-Reasoning-Qwen-1.5B.

GPU Requirements for RL Agent Training

RL training for agents is compute-intensive. You need GPUs for both:

1. LLM inference during rollouts (generating agent actions)

2. Model training (updating weights from collected trajectories)

Minimum practical setup:

• Small models (1.5B-4B): One RTX 4090 24GB or RTX 5090 32GB can handle both inference and training for research experiments
• Medium models (8B): Multiple GPUs or a cloud multi-GPU instance. Vast.ai offers multi-4090 setups from ~$0.80/hr
• Large models (14B+): A100/H100 cluster territory — ProRL Agent's decoupled design shines here since you can scale rollout and training nodes independently

For most developers experimenting with RL agent training, the 1.5B BroRL model is the practical starting point. It fits on a single consumer GPU and demonstrates the core concepts.

See our Best GPUs for Running AI Locally guide for GPU selection, or check which local LLMs work best on RTX 50-Series cards.

Who Should Care About ProRL Agent

• ML engineers training agents — if you're doing RL on multi-turn tasks (coding, math, tool use), ProRL Agent removes the rollout infrastructure burden
• Research teams on shared clusters — rootless sandboxing means no more fighting IT for Docker access on HPC nodes
• Teams evaluating different RL trainers — the framework-agnostic HTTP API lets you swap trainers without rebuilding your environment setup
• AI coding tool developers — the SWE-bench results show this approach makes smaller models significantly more capable at real software engineering tasks

If you use Claude Code, Cursor, or Copilot for coding, the research behind ProRL Agent is part of what makes AI coding assistants better over time. For a comparison of current AI coding tools, see our Claude Code vs Cursor vs GitHub Copilot guide.

Links

• Paper: ProRL Agent (arXiv)
• NVIDIA Blog: BroRL
• BroRL Model on HuggingFace

*Training AI agents locally? You'll need serious GPU power. Check our Best GPUs for Running AI Locally guide, or rent GPU clusters on Vast.ai starting at $0.20/hr per card.*

2. Rollout Service

The Rollout Service manages the execution of agent tasks in the sandbox environments. It handles the API requests from the training loop, orchestrates the execution of tasks, and returns the generated trajectories. This service is designed to be highly scalable and can be distributed across multiple nodes if necessary. For instance, if you are using an RTX 5090 with 24GB VRAM, you can deploy multiple sandbox environments to fully utilize the GPU resources without overloading the system.

3. Training Loop

The Training Loop is responsible for updating the model based on the trajectories received from the Rollout Service. By decoupling the rollout execution from the training loop, ProRL Agent allows for more efficient use of resources. This separation means that the training loop can focus solely on optimizing the model, while the Rollout Service handles the I/O-intensive task of generating trajectories. This leads to faster training times and better model performance.

Practical Examples

To illustrate how ProRL Agent can be used in practice, consider the following scenarios:

Scenario 1: Training a Chatbot

Imagine you are developing a chatbot that needs to handle customer inquiries. Using ProRL Agent, you can set up a sandbox environment that simulates customer interactions. The AgentHandler interface can be used to define the initialization, execution, and evaluation methods for the chatbot. The Rollout Service will generate interaction trajectories, and the Training Loop will update the chatbot's model based on these trajectories. This setup can be particularly useful for training chatbots in a realistic environment, ensuring they perform well in real-world scenarios.

Scenario 2: Autonomous Vehicle Navigation

For autonomous vehicle navigation, ProRL Agent can be used to simulate driving scenarios. The sandbox environment can include various driving conditions, such as traffic, weather changes, and road obstacles. The AgentHandler can be configured to handle the initialization, execution, and evaluation of the driving tasks. The Rollout Service will generate driving trajectories, and the Training Loop will update the navigation model accordingly. This approach allows for efficient and effective training of autonomous vehicles, improving their safety and reliability.

Benchmarks

ProRL Agent has been tested extensively in various reinforcement learning tasks, and the results are promising. In one benchmark, an 8B parameter model trained with ProRL Agent achieved a 19.5% improvement in SWE-bench Verified score compared to a baseline model trained with a traditional tightly coupled approach. This improvement highlights the effectiveness of ProRL Agent in enhancing model performance.

How-to Steps

If you are interested in implementing ProRL Agent in your projects, follow these steps:

1. Set Up the Environment: Clone the ProRL Agent repository from GitHub and install the necessary dependencies. Ensure that your RTX 50-Series GPU is properly configured and that you have the required VRAM.

2. Define the AgentHandler: Implement the AgentHandler interface to define the initialization, execution, and evaluation methods for your specific task. This step is crucial as it sets up the environment in which the agent will operate.

3. Configure the Rollout Service: Set up the Rollout Service to manage the execution of agent tasks in the sandbox environments. Configure the service to handle API requests from the Training Loop and return the generated trajectories.

4. Implement the Training Loop: Develop the Training Loop to update the model based on the trajectories received from the Rollout Service. Ensure that the loop is optimized for performance and can handle the workload efficiently.

5. Run the Training Process: Start the training process by initiating the Rollout Service and the Training Loop. Monitor the training progress and adjust the parameters as needed to achieve the desired model performance.

Key Takeaways

• ProRL Agent decouples rollout execution from the training loop, leading to more efficient use of resources and better model performance.
• The framework is open-source and can be integrated into NVIDIA NeMo Gym, making it accessible for a wide range of applications.
• ProRL Agent can be used in various scenarios, such as training chatbots and autonomous vehicle navigation, to simulate realistic environments and improve model performance.

For more information on reinforcement learning and AI agent training, check out our article on Advanced Reinforcement Learning Techniques.

By leveraging ProRL Agent, you can significantly enhance the efficiency and effectiveness of your AI agent training processes, leading to better-performing models and more robust applications.

Frequently Asked Questions

What is ProRL Agent and how does it improve AI training?

ProRL Agent is an open-source infrastructure framework by NVIDIA that separates AI agent rollout execution from reinforcement learning (RL) training, allowing for more efficient use of resources and better scalability compared to traditional tightly coupled systems.

How does ProRL Agent address resource conflicts during AI training?

ProRL Agent addresses resource conflicts by decoupling I/O-heavy rollout execution from GPU-heavy training, thus optimizing the use of both resources and improving overall efficiency.

Can ProRL Agent be integrated with different RL trainers?

Yes, ProRL Agent is designed to be portable, meaning it can be integrated with various RL trainers without the need to rewrite the rollout infrastructure.

What are the main benefits of using ProRL Agent over other RL frameworks?

ProRL Agent offers benefits such as improved resource utilization, enhanced portability across different RL trainers, and reduced friction in high-performance computing (HPC) environments, making it a superior choice for AI training.

Is ProRL Agent free to use, and what are the costs involved?

ProRL Agent is open-source, so it is free to use. However, users may incur costs related to the computational resources (like GPUs) required for training AI models.

What are some alternatives to ProRL Agent for AI training?

Alternatives to ProRL Agent include other RL frameworks such as SkyRL-Agent, VeRL-Tool, and Agent Lightning, which, while functional, may not offer the same level of decoupling and efficiency improvements.