> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Write an RL loop with the Loops SDK

> Build a reinforcement learning loop on Loops: roll out completions from the paired sampler, score them into advantages, and step the trainer.

An RL loop on Loops runs on the client. Your script drives it: roll out completions from the paired sampler, score them into advantages, accumulate gradients with [`forward_backward()`](https://docs.baseten.co/reference/sdk/loops/training-client), and apply them with [`optim_step()`](https://docs.baseten.co/reference/sdk/loops/training-client). This page walks through one full pass. See [Loops concepts](https://docs.baseten.co/loops/concepts.md) for how the trainer and sampler interact underneath, and [Loss functions](https://docs.baseten.co/loops/loss-functions.md) for the per-loss data contract.

## Prerequisites

* A [workspace API key](https://docs.baseten.co/organization/api-keys.md), set as `BASETEN_API_KEY`.
* Python 3.12+ and [uv](https://docs.astral.sh/uv/) to install the SDK and run the script.

## Install

Install `baseten-loops` into a uv project. Create one first if you don't have it:

```bash theme={"system"}
uv init loops-rl
cd loops-rl
uv add baseten-loops
```

## Provision the run

The SDK creates the run and runs it in the serverless or dedicated mode according to your workspace access and model configuration.

An RL run samples on every step. Set `with_sampler=True` to create the paired sampler with the trainer. For dedicated runs, this overlaps GPU startup instead of delaying sampler provisioning until the first rollout:

```python rl_loop.py theme={"system"}
from baseten.loops import ServiceClient

training_client = ServiceClient().create_lora_training_client(
    base_model="Qwen/Qwen3-8B",
    with_sampler=True,
    name="rl-example",  # without a name, the run shows up as the base model ID
)
# Publish the current weights and get a sampling client.
sampling_client = training_client.save_weights_and_get_sampling_client()
```

See [Availability modes](https://docs.baseten.co/loops/availability-modes/overview.md) for how a run's mode affects billing.

## Roll out completions

Fan out one group of completions per prompt. A GRPO-style group means one prompt, many completions, advantages computed relative to the group:

```python rl_loop.py theme={"system"}
import asyncio

from baseten.loops import ModelInput, SamplingParams

tokenizer = training_client.get_tokenizer()
prompt_ids = tokenizer.encode("What is 17 times 24?", add_special_tokens=False)
group_size = 4

prompt = ModelInput.from_ints(prompt_ids)


async def rollout():
    return await asyncio.gather(
        *[
            sampling_client.sample_async(
                prompt,
                sampling_params=SamplingParams(temperature=1.0, top_p=0.95),
            )
            for _ in range(group_size)
        ]
    )


# asyncio.run() drives the fan-out from a plain script.
responses = asyncio.run(rollout())
```

Each response carries `sequences`, each with the sampled `tokens`, per-token `logprobs`, and `stop_reason`. Pass `include_prompt_logprobs=True` when you need the prompt's per-position logprobs too.

On deployments with sticky routing enabled, `affinity_key` pins every request that shares a key to one sampler replica. It is a keyword argument on `sample()` and `sample_async()`, not a field on `SamplingParams`. Set it to one key per rollout group so the group's shared prompt stays in one replica's prefix cache; don't reuse one key across a whole run, or every group lands on the same replica. Weight selection follows the sampling client's checkpoint or policy-version settings:

```python rl_loop.py theme={"system"}
async def rollout():
    return await asyncio.gather(
        *[
            sampling_client.sample_async(
                prompt,
                sampling_params=SamplingParams(temperature=1.0, top_p=0.95),
                affinity_key="rollout-group-0",
            )
            for _ in range(group_size)
        ]
    )
```

On serverless runs, the sampling client pins the checkpoint saved by `save_weights_and_get_sampling_client()`. Publishing later weights doesn't change an existing client's checkpoint. Before the first optimizer step, the client samples the base model.

On dedicated runs, the sampling client carries a minimum policy version set by `save_weights_and_get_sampling_client()`. Sampling waits until the sampler serves at least that version, then uses the live adapter, which can advance after later publishes. A replica below the floor returns `408`, and the SDK retries with backoff until the replica serves the requested version or the client's ready timeout expires.

## Score rewards into advantages

Score each completion, then normalize within its group: subtract the group mean, and divide by the group's standard deviation for unit-variance advantages. When every completion in a group earns the same reward the standard deviation is zero, so guard the division; those advantages are all `0.0`, which makes the group a no-op for the policy losses rather than an error.

```python rl_loop.py theme={"system"}
sequences = [response.sequences[0] for response in responses]
scores = [1.0 if "408" in tokenizer.decode(seq.tokens) else 0.0 for seq in sequences]

mean = sum(scores) / len(scores)
std = (sum((score - mean) ** 2 for score in scores) / len(scores)) ** 0.5
advantages = [(score - mean) / std if std else 0.0 for score in scores]
```

Each completion becomes one [`Datum`](https://docs.baseten.co/reference/sdk/loops/types) carrying the full sequence and two per-position tensors: the rollout logprobs (the behavior policy) and the advantages, tiled across the completion's token positions. Prompt positions carry `0.0` in both tensors.

```python rl_loop.py theme={"system"}
from baseten.loops import Datum, TensorData

prompt_len = len(prompt_ids)


def build_datum(sequence, advantage):
    tokens = prompt_ids + sequence.tokens  # prompt + completion
    L = len(tokens)
    return Datum(
        model_input=ModelInput.from_ints(tokens),
        loss_fn_inputs={
            "logprobs": TensorData(
                data=[0.0] * prompt_len + sequence.logprobs, dtype="float32", shape=[L]
            ),
            "advantages": TensorData(
                data=[0.0] * prompt_len + [advantage] * len(sequence.tokens),
                dtype="float32",
                shape=[L],
            ),
        },
    )


datums = [
    build_datum(sequence, advantage)
    for sequence, advantage in zip(sequences, advantages)
]
```

Advantages are per-position and must match the sequence length in every `Datum` in the batch. They also serve as the active-token mask for the policy losses: a position with advantage `0.0` contributes nothing to the loss or to the denominator, so a completion whose group-normalized advantage lands at zero drops out of the update.

When the rollout samples at a temperature other than `1.0`, set it on `SamplingParams`. There is no matching parameter on the trainer, so the sampler and the trainer can score the same token under different distributions; the divergence metrics in the next section show the drift.

## Step the trainer

Accumulate gradients, then apply them:

```python rl_loop.py theme={"system"}
from baseten.loops import AdamParams

fb = training_client.forward_backward(
    data=datums,
    loss_fn="ppo",  # or "cispo", "importance_sampling", "dppo", "dro"
).result(timeout=600.0)

training_client.optim_step(AdamParams(learning_rate=1e-4)).result(timeout=600.0)
```

The ratio-based losses need the per-position `logprobs` from above: the importance ratio is `exp(new_logprobs - logprobs)`, so the behavior policy each trajectory was sampled from has to travel with the datum. The losses differ in how they clip or penalize that ratio; see [Loss functions](https://docs.baseten.co/loops/loss-functions.md). For supervised fine-tuning, use `cross_entropy` with `target_tokens` instead, which reads neither `logprobs` nor `advantages`.

`forward_backward()` adds to the trainer's gradient buffer, so you can call it several times between `optim_step()` calls to accumulate gradients across microbatches or rollout batches. Pass `top_k_logprobs` (up to `20`) to get the top token log-probabilities per active position back in the result, which is useful for debugging the ratio.

Invalid `loss_fn_config` values fail fast: the server validates the config before running the pass and returns the validation error verbatim.

## Publish weights for the sampler

Call [`save_weights_and_get_sampling_client()`](https://docs.baseten.co/reference/sdk/loops/training-client) after `optim_step()` and use its returned client for the next rollout. The method returns a `SamplingClient` directly:

```python rl_loop.py theme={"system"}
sampling_client = training_client.save_weights_and_get_sampling_client("default")
```

On serverless runs, each call creates a checkpoint with a unique name and returns a client pinned to it. Use the client's `checkpoint_path` and `checkpoint_name` to identify that save rather than reconstructing them from `"default"`. Keep using the returned client for that rollout group, and reacquire a client after the next training update. On dedicated runs, the returned client sets a minimum policy version rather than pinning an exact checkpoint.

[`save_weights_for_sampler()`](https://docs.baseten.co/reference/sdk/loops/training-client) saves weights without updating the paired sampler's live policy. Its future resolves to a version and path. An existing serverless sampling client keeps its checkpoint, and an existing sampling client in the dedicated mode keeps its original minimum version.

You can submit rollout requests while training is in progress. Those rollouts can use weights from before the latest optimizer step, which is why the ratio-based losses take the rollout logprobs: the importance ratio corrects for the gap between the policy that sampled and the policy being trained.

## Evaluate without training

[`forward()`](https://docs.baseten.co/reference/sdk/loops/training-client) runs the same loss math with no backward pass and leaves the gradient buffer untouched, so it is safe to interleave with gradient accumulation. Use it to evaluate a held-out batch mid-run without disturbing the gradients you have accumulated.

## Monitor KL divergence between the sampler and trainer

RL on Loops runs the policy in two places: the sampler rolls out and the trainer learns. Both should assign the same log-probability to every sampled token, so in a healthy run the KL divergence between them stays near zero. A rising KL divergence means the sampler is serving stale weights or its numerics differ from the trainer's.

The SDK ships [`loops_log_kl_sample_train()`](https://docs.baseten.co/reference/sdk/loops/helpers), which logs a train/sample divergence panel to Weights & Biases: the `kl_sample_train_v1_loops`, `kl_sample_train_v2_loops`, and `kl_sample_train_k3` estimators (Tinker's first- and second-order estimators plus Schulman's k3), importance-ratio and effective-sample-size statistics, per-token difference histograms, and a worst-divergent-tokens table.

## Checkpoint mid-run

Publish the current weights and get a sampling client with [`save_weights_and_get_sampling_client()`](https://docs.baseten.co/reference/sdk/loops/training-client), or persist a restorable checkpoint with [`save_state("step-1")`](https://docs.baseten.co/reference/sdk/loops/training-client), which takes a name for the checkpoint. A serverless sampling client selects the saved checkpoint; in the dedicated mode, the paired sampler picks up published weights without restarting. See [How weight sync works](https://docs.baseten.co/loops/concepts.md#weight-sync). To resume a run from a checkpoint, see [Checkpoints](https://docs.baseten.co/loops/concepts.md#checkpoints).

## Shut down the run

After saving the checkpoints you need, close the local SDK clients. Append:

```python rl_loop.py theme={"system"}
sampling_client.close()
training_client.close()
```

The `Qwen/Qwen3-8B` model in this example uses dedicated capacity. Closing the clients doesn't deactivate the run. To stop GPU billing, use the run ID from `training_client.run_id`. With the [Baseten CLI](https://docs.baseten.co/reference/cli/baseten/overview.md#install) installed, end the run:

1. Deactivate the run, and confirm when prompted:

   ```bash theme={"system"}
   baseten loops run deactivate --run-id "<run_id>"
   ```

2. Check that the run's `status.name` is `INACTIVE`:

   ```bash theme={"system"}
   baseten loops run describe --run-id "<run_id>" --output json
   ```

Saved checkpoints remain available. See [Deactivate a run](https://docs.baseten.co/loops/deactivate-a-run.md) for the REST API option and serverless behavior if you change the model.

## Next steps

* [Loss functions](https://docs.baseten.co/loops/loss-functions.md) for the full per-loss data contract and `loss_fn_config` defaults.
* [`TrainingClient` reference](https://docs.baseten.co/reference/sdk/loops/training-client) for `forward_backward()`, `forward()`, and `optim_step()` signatures.
* [`SamplingClient` reference](https://docs.baseten.co/reference/sdk/loops/sampling-client) for `sample()`, `compute_logprobs()`, and `affinity_key`.