Secure Reinforcement Fine-Tuning

Use reinforcement fine-tuning while keeping sensitive components and data under your control. This composes BYOB storage with a reward pipeline and rollout infrastructure that stay in your environment.

Configure storage (BYOB)

Set up your dataset storage using GCS, AWS S3, or Azure Blob Storage on the BYOB page.For models, you can optionally use External AWS S3 Bucket Integration.

Prepare your reward pipeline and rollouts

Keep your reward functions, rollout servers, and training metrics under your control. Generate rewards from your environment and write them to examples in your dataset (or export a dataset that contains per-example rewards).

Reward functions and reward models remain proprietary and never need to be shared
Rollouts and evaluation infrastructure run in your environment
Model checkpoints can be registered to your storage registry if desired

Create a dataset that includes rewards

Create or point a Dataset at your BYOB storage. Ensure each example contains the information required by your reward pipeline (for example, prompts, outputs/trajectories, and numeric rewards).

You can reuse existing supervised data by attaching reward signals produced by your pipeline, or export a fresh dataset into your bucket for consumption by RFT.

Run reinforcement fine-tuning step from Python

Use the Python SDK to create a reinforcement fine-tuning step that reads from your BYOB dataset and produces a new checkpoint.

from fireworks import Fireworks

client = Fireworks()

# Create a reinforcement fine-tuning step
step = client.reinforcement_fine_tuning_steps.create(
    rlor_trainer_job_id="my-rft-job-001",
    display_name="Secure RFT Training Step",
    training_config={
        "base_model": "accounts/fireworks/models/{BASE_MODEL}",
        "learning_rate": 1e-5,
        "lora_rank": 8,
        "max_context_length": 4096,
        "batch_size_samples": 32,
    },
    dataset="accounts/{ACCOUNT}/datasets/{DATASET_NAME}",  # Your BYOB dataset with rewards
    output_model="accounts/{ACCOUNT}/models/my-improved-model-v1",
    reward_weights=["score"],  # Field name for rewards in your dataset
)

# Poll for completion
import time
timeout = 3600  # 1 hour timeout
start_time = time.time()
while True:
    if time.time() - start_time > timeout:
        raise TimeoutError(f"Job polling timed out after {timeout} seconds")
    job = client.reinforcement_fine_tuning_steps.get(
        rlor_trainer_job_id="my-rft-job-001"
    )
    if job.state == "JOB_STATE_COMPLETED":
        print("Training complete!")
        break
    elif job.state in ("JOB_STATE_FAILED", "JOB_STATE_CANCELLED"):
        raise RuntimeError(f"Training failed: {job.state}")
    time.sleep(10)

See the Create Reinforcement Fine-tuning Step API reference for full parameters and options.

For a complete iterative RL workflow example using the Python SDK, including rollout generation, reward computation, and hot-reloading LoRA adapters, see the iterative RL workflow example on GitHub.

When continuing from a LoRA checkpoint, training parameters such as lora_rank, learning_rate, max_context_length, and batch_size_samples must match the original LoRA training.

Verify outputs and enforce controls

Validate the new checkpoint functions as expected in your environment
If exporting models to your storage, apply your registry policies and access reviews
Review audit logs and rotate any temporary credentials used for the run

Do not store long-lived credentials in code. Use short-lived tokens, workload identity, or scoped service accounts when granting Fireworks access to your buckets.

You now have an end-to-end secure RFT workflow with BYOB datasets, proprietary reward pipelines, and isolated training jobs that generate new checkpoints.

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Secure Reinforcement Fine-Tuning