Model training in artificial intelligence has evolved dramatically in recent years, and Reinforcement Learning sits right at the heart of that transformation.

Where teams once relied almost entirely on well-crafted prompts and static datasets to get an AI agent to behave as expected, today there is a more robust path — one that goes beyond trial and error and starts shaping model behavior from the inside out. 🔧

This matters especially now, as companies across different industries are looking for specialized agents capable of executing complex tasks with greater precision, whether that means triaging security alerts, running data analyses, providing customer support, or automating workflows via the command line. Open models fit nicely into this picture because they offer more control over data, intellectual property, and deployment, while RL turns domain-specific success criteria into concrete training signals.

The good news is that leading labs have already shown in practice that this approach works at scale. OpenAI used large-scale RL to train its o-series models, DeepSeek-R1 demonstrated how Group Relative Policy Optimization (GRPO) and verifiable rewards can improve reasoning, math, and code, and NVIDIA went even further with Nemotron 3 Super, which underwent post-training with RL across multiple environments, covering 21 NeMo Gym verifiers and 37 datasets, generating roughly 1.2 million environment rollouts.

What Reinforcement Learning means in the context of AI agents

Reinforcement Learning is a training approach where the model learns through trial, receiving reward or penalty signals depending on the quality of the generated response. Unlike supervised fine-tuning, where you provide correct input-output pairs, in RL the model explores different outputs and adjusts its behavior based on what works best within a defined set of criteria. This cycle of exploration and adjustment is what makes RL so powerful for building specialized agents, because the model learns to optimize outcomes rather than simply imitate examples.

An analogy helps here: think of the model as the agent’s brain, the agent framework as its body, and the tools as the workspace where it can act. Improving the framework or adding tools helps in many cases, but it does not always change the model’s behavior itself. If the agent keeps making the same mistakes when calling tools, fails in long workflows, formats outputs incorrectly, or picks the wrong strategies, you need a training signal. And that is exactly where RL finds its place. 🎯

In practice, what sets this approach apart is the presence of a verifier — an automated system, a mathematical function, or even another model — responsible for evaluating whether the agent’s output achieved the expected goal. This verifier can take many forms: automated tests, tool execution, schema validation, simulators, reward models, LLM-as-judge style review, human preference labels, or any other task-specific feedback. When this verifier can operate reliably and at scale, the model training cycle becomes much more efficient because you no longer need human annotators for every single example.

DeepSeek-R1 is a concrete example. By using rewards based on verifiable correctness for math and code tasks, the model was able to develop emergent reasoning behaviors without requiring human supervision at every training step. That does not mean humans left the process entirely — rather, their role shifted. Instead of labeling each response individually, they designed the criteria and verifiers that guide the agent’s learning across thousands of iterations.

RAG, prompting, SFT, and RL: when to use each approach

A common mistake is starting a project by asking which algorithm to use. The more productive question is: what behavior do I want to improve, and how will I measure it? Once you answer that, picking the right technique for each situation becomes much easier. Here is how different scenarios call for different solutions:

• If the model lacks domain facts, start with RAG or data injection.
• If it does not follow a specific format, try prompting first, then SFT.
• If it needs to imitate examples, use SFT, with LoRA or QLoRA for efficient adaptation.
• If you have preferred versus rejected outputs, DPO is the way to go.
• If you can verify success algorithmically, go with RLVR using GRPO.
• If you need alignment with more nuanced human preferences, lean into RLHF or reward modeling.
• If the agent fails in long workflows involving many tool calls, state updates, or conversation turns, the ideal approach is environment-based RL with trajectory-level rewards.

Understanding SFT, DPO, RLVR, and RLHF in practice

SFT makes sense when you have demonstrations of the desired behavior — things like following instructions, multi-turn conversations, output schemas, tool-calling formats, or domain workflows. DPO comes into play when you have preference pairs where one response is clearly better than another. RLHF is the right call when more subtle human preferences cannot be captured by rules and you have the infrastructure for preference data, reward models, and careful training pipelines. RLVR, on the other hand, shines when correctness can be checked algorithmically — valid JSON, correct CLI commands, passing tests, exact math answers, or successful tool calls.

For verifiable tool use and agent workflows, a common starting path tends to be: apply SFT if needed, then GRPO with verifiable rewards, evaluate, inspect failures, and repeat the cycle. The best technique always depends on the type of signal you have available.

Why GRPO is a good starting point

For RLVR workflows, GRPO tends to be quite practical as a starting point. It generates multiple completions per prompt, scores each one with a verifier, and updates the model based on relative performance within the group. Compared to PPO-style RLHF, GRPO has fewer moving parts and works naturally with rule-based rewards, which has turned it into a go-to method for many agentic RL setups.

Newer variants keep emerging as training systems mature. DAPO builds on GRPO with dynamic sampling and asymmetric clipping to preserve useful learning signal and exploration diversity, while GSPO optimizes at the sequence level instead of the token level, improving training stability — especially for Mixture-of-Experts (MoE) models.

When it makes sense to use RL for training agents

Not every use case justifies the effort of building a Reinforcement Learning pipeline. The starting point for evaluating this is checking whether the agent’s domain has reliably verifiable outcomes. If you can create a function or system that objectively says whether the agent’s response was good or bad, you have the key ingredient to get started. Tasks like code execution, structured data analysis, alert triage with well-defined criteria, and SQL query generation are examples where this kind of verification is quite feasible.

On the flip side, domains where response quality is highly subjective — like open-ended creative writing or free-form emotional support — still depend much more on supervised approaches or RLHF with direct human feedback. Pure RL really shines when the success criteria can be expressed objectively and when the agent’s action space is sufficiently bounded for training to converge without blowing up compute costs. That clarity of scope is what separates successful projects from expensive experiments that never go anywhere.

One important detail worth reinforcing: RL works best when the model can already sometimes produce the right behavior but does not do so reliably. If the reward is poorly designed, RL will optimize the wrong behavior just as efficiently as it would optimize the right one. That is why starting with evaluation before training is a practice that saves a lot of time and resources down the road.

How to set up a minimal verifiable training loop

A minimal training loop with Reinforcement Learning for specialized agents can be broken down into seven components: the policy model you are training, the task that serves as input, the action the model produces as output, the environment that executes that action and returns feedback, the verifier that scores success, the rollouts — which are the sampled attempts from the current model — and finally the policy update that adjusts weights to increase the likelihood of better outputs. Each of these elements needs to be well-defined before kicking off any training run, because a failure in any one of them will compromise the entire process.

The environment can be as simple as a Python code execution sandbox or as complex as a network infrastructure simulator. For simpler single-turn tasks, a static dataset may even be enough. But for agentic RL spanning multi-step, multi-turn workflows, you need an environment that defines a dataset, agent framework, verifier, and state. A long-running code agent might need several tool calls before the tests pass, while a data analysis agent might need to inspect files, run queries, generate charts, and validate results.

NVIDIA’s NeMo Gym is an example of how this can be structured at scale, offering a reproducible way to build environments that connect agents, models, external systems, tools, and verifiers — with tutorials covering single-step scenarios, multi-step flows, stateful environments, real-world use cases, and LLM-as-judge verification. This significantly reduces the engineering work needed to build this pipeline from scratch.

Reward design: start simple

Reward design is where many RL projects get unnecessarily complicated. The golden rule is to start with the simplest reward that proves the loop works. For RLVR, this can be as straightforward as a binary reward: plus one if the output passes the verifier, zero otherwise. Intermediate signals should only be added when they genuinely measure real progress — like scoring the right tool choice, producing a valid intermediate artifact, or passing a partial test.

Good reward functions have three essential properties: they measure the actual task, they are hard to game, and they fail visibly when something is wrong. Before training, run your reward function against 50 to 100 model outputs and inspect the scores manually. If the reward disagrees with your judgment, the problem is the reward — and that is what needs fixing first. Over-shaping can teach the model to optimize a checklist instead of actually solving the task.

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Planning compute costs for training and rollouts

The cost of RL comes from two main workloads: rollouts and training. Both are affected by batch size, model size, and sequence length. Rollout cost specifically scales with the number of tool calls, conversation turns, and environment steps, while training cost scales with dataset size and the number of policy updates. Inference engines like vLLM help reduce rollout latency, while training frameworks improve throughput.

GPU requirements vary significantly depending on the use case. A small adapter-based experiment with models in the 1B to 8B parameter range can start on a single modern GPU or a small multi-GPU node. Larger models, full fine-tuning, long-context tasks, high rollout counts, or multi-step agentic environments will require multiple GPUs. When compute is limited, the move is to first reduce model size, maximum token count, generations per prompt, and the number of parallel environments. Starting small is always a solid strategy because smaller models are great for debugging data, verifiers, environments, and the training loop itself.

NVIDIA ecosystem tools to put this into practice

NVIDIA has built a pretty comprehensive ecosystem for anyone looking to work with model training using Reinforcement Learning. NeMo RL provides components for scalable post-training with RL, including support for algorithms like GRPO, along with native integration with NeMo Gym verifiers. This means you can build a complete end-to-end pipeline without having to stitch together multiple tools from different vendors, which significantly reduces the operational complexity of the project. The ecosystem also plays well with popular community tools like OpenRLHF, SGLang, Unsloth, veRL, and vLLM.

NeMo Gym, specifically, works as a collection of ready-to-use environments and verifiers for tasks ranging from math and code to tool use and structured reasoning. The 21 verifiers used in training Nemotron 3 Super cover a broad range of capabilities, which allowed the model to develop robust skills across multiple domains within a single post-training process. For teams just getting started with RL, these pre-built verifiers are a valuable starting point because they eliminate the need to build this critical component from scratch.

On top of that, NeMo Data Designer helps with synthetic data generation, letting you create structured task datasets from scratch or from seed data, control relationships between fields, and validate outputs against specifications. This is especially useful when real-world examples are scarce, since you can generate task variants, edge cases, and tool-calling scenarios, then filter everything with validators or automated review. Keep in mind that synthetic data is not ground truth — maintain a small, human-quality seed set, deduplicate aggressively, and inspect failure cases before using them as training data. 🚀

The path to high-performance specialized agents runs through mastering these Reinforcement Learning techniques, and the ecosystem of tools available today makes that path more accessible than it was just a few years ago. Think of this process as a continuous improvement flywheel: production failures become evaluations, evaluations become environments, environments generate rewards, rewards improve the model, and the next model gets tested before any deployment. The ingredient that still makes all the difference is clarity about what the agent needs to do and how to measure whether it is doing it well — because without well-defined verifiable rewards, even the best training pipeline will lose its direction.