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+---
+title: "ART 101"
+description: "Conceptual walkthrough"
+icon: "academic-cap"
+---
+
+This section introduces the minimal conceptual vocabulary you need to train models using GRPO in ART and Serverless API. Serverless aims to provide a far
+better developer experience for experienced practitioners and RL newbies alike. Though we want to simplify the ease of training models with RL, we also do
+not want to make it sound like a black box. This walkthrough aims to give you an overview of the building blocks required to train RL models using Serverless API. 
+
+
+## 1. Client-server training loop
+
+ART's functionality is divided into a client and a server.
+
+A **client** (your Python process):
+  - runs your agent workflow
+  - requests completions from an OpenAI-compatible API,
+  - stores the interaction into trajectories,
+  - computes rewards and submits them for training.
+
+A **server** (GPU-backed):
+  - accepts the requests (a new batch of samples) submitted by the client to train the model
+  - trains the models on the samples using GRPO. The state of the server is either initialized from the latest checkpoint or an empty one on first iteration.
+  - leverages LoRA to train the model. Inference is blocked while training model on the latest batch 
+  - saves and loads updated LoRA checkpoints, then unblocks inference.
+  - serves the latest checkpoint for inference.
+
+**Note:** Serverless API uses [W&B Inference]((https://docs.wandb.ai/inference)) cluster. Hence, you would require to set your `WANDB_API_KEY` before using it.
+
+
+## 2. Model - Selection, Setup, and Registration
+
+Serverless API as of today supports the following models:
+- [OpenPipe Qwen 3 14B Instruct](https://huggingface.co/OpenPipe/Qwen3-14B-Instruct)
+- [Qwen 3 30B A3B Instruct](https://huggingface.co/Qwen/Qwen3-30B-A3B)
+
+Depending upon the use case and the needs, you can choose either of the model for your workflow. A trainable model is an instance of the `art.TrainableModel(..)`
+class and it takes the following arguments as the inputs:
+- *project*: The workflow will be initialized inside the W&B workspace with this name.
+- *name*: The name of the final fine-tuned model. The scope of this name is similar to the scope of W&B runs within a project.
+- *base_model*: The base model to start from. Choose any of the above two. 
+
+Here is an example demonstrating how to instantiate and register a model with the serverless backend. 
+
+```python
+import art
+from art.serverless.backend import ServerlessBackend
+import wandb
+
+
+PROJECT_NAME = "math-agent"
+MODEL_NAME = "qwen3-math-001"
+BASE_MODEL = "OpenPipe/Qwen3-14B-Instruct"
+
+# Init your project in wandb
+wandb.init(project=PROJECT_NAME)
+
+# Set up the trainable model and the training backend in ART
+model = art.TrainableModel(
+        name=MODEL_NAME,
+        project=PROJECT_NAME,
+        base_model=BASE_MODEL,
+)
+
+# We will use serverless backend to train our model.
+backend = ServerlessBackend()
+
+# Register the model with the backend
+await model.register(backend)
+```
+
+## 3. Trajectory and TrajectoryGroup
+
+When training a model, we generate rollouts at every step. A rollout in the LLM world is the response or a group of responses generated by a LLM for a given
+input (prompt). A `Trajectory` object contains a set of system, user, and assistant messages that are produced by the model or the agent in a single rollout. 
+The messages are stored in a `messages_and_choices` sequence. A trajectory object can also contain additional information like reward assigned to a rollout,
+the training step number as metadata or the optional [additional histories](https://art.openpipe.ai/features/additional-histories).
+
+A `TrajectoryGroup` object contains a set of `Trajectory` objects corresponding to a single input example. For example, if you are generating four output
+samples per input sample at training step `t`, then each rollout is represented by a `Trajectory` object, and the `TrajectoryGroup` will store all such four
+objects.
+
+Here is an example showcasing how to store rollouts in a `Trajectory` object.
+
+```python
+client = openai.AsyncOpenAI(
+    # We need to provide the endpoint where the model is hosted
+    base_url=model.inference_base_url,
+    api_key=model.inference_api_key,
+)
+
+async def rollout(client, model, task_input, step, completion_args):
+    """Generates a rollout for a given input.
+
+    Args:
+        client: OpenAI compatible client required to generate responses
+            from a model endpoint
+        model: An instance of the (trainable) model
+        task_input: Input text for the model
+        step: Current training step
+        completion_args: (Optional) Any other completion args compatible
+            with the OpenAI client API
+
+    Returns:
+        A `Trajectory` instance containing the rollouts, and any other
+        metadata
+
+    """
+
+    traj = art.Trajectory(
+        # We assign the actual reward value to a trajectory after evaluating
+        # it. This is why it is set to 0 during rollout phase.
+        reward=0.0,
+        messages_and_choices=[
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": task_input.question}
+        ],
+        metadata={"step": step,},
+    )
+
+    response = await client.chat.completions.create(
+        model=model.get_inference_name(),
+        messages=traj.messages(), # Extract the messages sequence
+        **completion_args
+    )
+    traj.messages_and_choices.append(response.choices[0])
+    return traj
+```
+
+## 4. Rewards calculations and assignment
+
+Once we have generated the trajectories for an input sample, we want to evaluate how good they are and assign them rewards accordingly. Rewards definition
+depends on the use case at hand. The only constraint one needs to be aware of is that RL in LLMs works best with verifiable rewards. There are ongoing research
+efforts to bridge the gap between verifiable and non-verifiable rewards.
+
+Here is an example of how we can define rewards for our math agent. You can modify these as per your use case.
+
+```python
+def normalize_number_str(num):
+    """Removes , from number represented as a string.
+    e.g. 2,00,000 -> 200000
+    """
+
+    if num is None:
+        return None
+    return num.strip().replace(",", "")
+
+def extract_guess(text):
+    """Extract the answer within the solution tags.
+    e.g. <solution>34</solution> -> 34
+    """
+
+    match = re.search(rf"{solution_start}(.*?){solution_end}", text, re.DOTALL)
+    if match:
+        content = match.group(1).strip()
+        nums = re.findall(r"[-+]?\d[\d,]*(?:\.\d+)?", content)
+        if nums:
+            return normalize_number_str(nums[-1].strip())
+
+    nums = re.findall(r"[-+]?\d[\d,]*(?:\.\d+)?", text)
+    if nums:
+        return normalize_number_str(nums[-1].strip())
+
+    return None
+
+
+def score_on_format(text):
+    """Check the correctness of format in the generated response.
+
+    Here we are interested in ensuring that the model generates
+    responses with solution_start and solution_end tags e.g.
+    <solution>...</solution>. A correct response gets a reward
+    of 1.0 and 0.0 otherwise.
+    """
+
+    has_solution_start = text.count(solution_start) == 1
+    has_solution_end = text.count(solution_end) == 1
+
+    if has_solution_start and has_solution_end:
+        solution_start_pos = text.find(solution_start)
+        solution_end_pos = text.find(solution_end)
+        if solution_start_pos < solution_end_pos:
+            return 1.0
+    return 0.0
+
+
+async def evaluate_rollouts(groups, questions, ground_truths):
+    """Evaluates the correctness of guessed answer wrt to ground truth
+
+    Here we evaluate each rollout in a batch against the corresponding
+    ground truth. If it matches exactly, we provide a high score say 10.0.
+    If it does not match exactly, but is an approximate match, we still
+    provide a positive score albeit a lower one. 
+    """
+
+    parsed = 0
+    for group in groups:
+        for trajectory in group.trajectories:
+            input_question = questions[parsed]
+            true_answer = ground_truths[parsed]
+            response = trajectory.messages_and_choices[-1].message.content
+
+            format_score = score_on_format(response)
+            guess = extract_guess(response)
+
+            if guess is None:
+                answer_score = 0.0
+            else:
+                if (guess == true_answer
+                    or 
+                    guess.strip() == str(true_answer).strip()
+                ):
+                    answer_score = 10.0
+                else:
+                    try:
+                        guess_num = float(normalize_number_str(guess))
+                        true_num = float(normalize_number_str(str(true_answer)))
+                        if true_num != 0:
+                            ratio = guess_num / true_num
+                            if 0.9 <= ratio <= 1.1:
+                                answer_score = 5.0
+                            elif 0.8 <= ratio <= 1.2:
+                                answer_score = 2.0
+                            else:
+                                answer_score = 0.0
+                        else:
+                            answer_score = 0.0
+                    except:
+                        answer_score = 0.0
+
+            # Once we evaluate a trajectory, we need to update the reward value
+            # associated with it. This will be used to update the policy accordingly.
+            trajectory.reward = format_score + answer_score
+            parsed += 1
+    return groups
+```
+
+## 5. Training and (Optional) validation loops
+
+This is the last piece that we need to put together to train our model. We registered our model with the backend, defined our rollout function to generate
+response corresponding to an input sample, defined a function to evaluate a batch of rollouts. A training loop involves the following steps:
+
+1. Sample the next batch from the dataset. A batch contains `n` samples.
+2. For each sample in batch, generate a `TrajectoryGroup`  group containing `m` rollouts wrapped in a `Trajectory` object. 
+3. Score the trajectories using the reward function as per the use case.
+4. Train the model on these scored trajectories
+5. (Optional) Delete the old checkpoints to save space
+
+Similar to the training loop, we can kickoff our validation at some interval. For example, you could validate your model performance on your validation
+data after every 50 steps in the training loop.
+
+Here is an how the training loop looks like when training with ART and Serverless API:
+
+```python
+from art.utils import iterate_dataset
+
+# Create a dataset iterator for your dataset
+iterator = iterate_dataset(
+        training_inputs,
+        groups_per_step=4,
+        num_epochs=1
+        initial_step=0,
+)
+ 
+for batch in data_iterator:
+    # 1. Sample the next batch
+    print(f"\nTraining step {batch.step}")
+    print(f"Batch contains {len(batch.items)} inputs")
+
+    input_questions = []
+    ground_truths   = []
+    train_groups    = []
+    rollouts_per_group = 4  # Number of rollouts per sample within a batch
+
+    # 2. For each sample in the batch, generate the trajectories/rollouts
+    for task_input in batch.items:
+        for _ in range(rollouts_per_group):
+            input_questions.append(task_input.question)
+            ground_truths.append(task_input.answer)
+        
+        train_groups.append(
+            art.TrajectoryGroup(
+                rollout(op_client, model, task_input, batch.step, completion_args=completion_args)
+                for _ in range(rollouts_per_group)
+            )
+        )
+    finished_groups = await art.gather_trajectory_groups(
+        train_groups,
+        pbar_desc="Generating rollouts",
+        max_exceptions=rollouts_per_group * len(batch.items),
+    )
+
+    # 3. Score the trajectories using the reward functions defined in your environment
+    finished_groups = await evaluate_rollouts(
+        finished_groups,
+        ground_truths=ground_truths,
+        questions=input_questions
+    )
+
+    # 4. Train the model on the current set of trajectories
+    await model.train(finished_groups, config=art.TrainConfig(learning_rate=3e-5))
+
+    # 5. Delete old checkpoints
+    await model.delete_checkpoints(best_checkpoint_metric="train/reward")
+    if batch.step >= max_training_steps:
+        print("Training complete!")
+        break
+```
+
+
+## Next steps
+
+For more resources and code examples, you can refer to these [notebooks](./notebooks.mdx)
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