Training Overview

Reinforcement Fine-Tuning (RFT) is free for models under 16B parameters. When creating an RFT job in the UI, filter for free tuning models in the model selection area on the fine-tuning creation page. If kicking off jobs from the terminal, you can find the model ID from the Model Library. Note: SFT and DPO jobs are billed per training token for all model sizes—see the pricing page for details.

The Eval Protocol CLI provides the fastest, most reproducible way to launch RFT jobs. This page covers everything you need to know about using eval-protocol create rft.

Before launching, review Training Prerequisites & Validation for requirements, validation checks, and common errors.

Already familiar with firectl? Use it as an alternative to eval-protocol.

Installation and setup

The following guide will help you:

Upload your evaluator to Fireworks. If you don’t have one yet, see Concepts > Evaluators
Upload your dataset to Fireworks
Create and launch the RFT job

Install Eval Protocol CLI

pip install eval-protocol

Verify installation:

eval-protocol --version

Set up authentication

Configure your Fireworks API key:

export FIREWORKS_API_KEY="fw_your_api_key_here"

Or create a .env file:

FIREWORKS_API_KEY=fw_your_api_key_here

Test your evaluator locally

Before training, verify your evaluator works. This command discovers and runs your @evaluation_test with pytest. If a Dockerfile is present, it builds an image and runs the test in Docker; otherwise it runs on your host.

cd evaluator_directory
ep local-test

If using a Dockerfile, it must use a Debian-based image (no Alpine or CentOS), be single-stage (no multi-stage builds), and only use supported instructions: FROM, RUN, COPY, ADD, WORKDIR, USER, ENV, CMD, ENTRYPOINT, ARG. Instructions like EXPOSE and VOLUME are ignored. See the RFT quickstart guide for details.

Create the RFT job

From the directory where your evaluator and dataset (dataset.jsonl) are located,

eval-protocol create rft \
  --base-model accounts/fireworks/models/qwen3-4b \
  --output-model my-model-name 

The CLI will:

Upload evaluator code (if changed)
Upload dataset (if changed)
Create the RFT job
Display dashboard links for monitoring

Expected output:

Created Reinforcement Fine-tuning Job
   name: accounts/your-account/reinforcementFineTuningJobs/abc123

Dashboard Links:
   Evaluator: https://app.fireworks.ai/dashboard/evaluators/your-evaluator
   Dataset:   https://app.fireworks.ai/dashboard/datasets/your-dataset
   RFT Job:   https://app.fireworks.ai/dashboard/fine-tuning/reinforcement/abc123

Monitor training

Click the RFT Job link to watch training progress in real-time. See Monitor Training for details.

Common CLI options

Customize your RFT job with these flags: Model and output:

--base-model accounts/fireworks/models/qwen3-4b  # Base model to fine-tune
--output-model my-custom-name                                   # Name for fine-tuned model

Training parameters:

--epochs 2                    # Number of training epochs (default: 1)
--learning-rate 5e-5          # Learning rate (default: 1e-4)
--lora-rank 16                # LoRA rank (default: 8)
--batch-size 65536            # RFT V1 packed-token budget; remains active with --batch-size-samples
--chunk-size 200              # Prompts rolled out per GRPO training step (default: 200). -1 disables chunking.

Loss method:

--rl-loss-method dapo           # RL loss method: grpo (default), dapo, gspo-token
--rl-kl-beta 0.001              # KL beta override (only for grpo; rejected for dapo/gspo-token)

Rollout (sampling) parameters:

--temperature 0.8               # Sampling temperature (default: 0.7)
--n 8                           # Number of rollouts per prompt (default: 4)
--response-candidates-count 8   # Alias for --n in firectl (default: 8, minimum: 2)
--max-tokens 4096               # Max tokens per response (default: 32768)
--top-p 0.95                    # Top-p sampling (default: 1.0)
--top-k 50                      # Top-k sampling (default: 40)
--max-concurrent-rollouts 64    # Max in-flight rollouts per job (default: 96, or the value set in @evaluation_test). Throughput only; no training effect.

Remote environments:

--remote-server-url https://your-evaluator.example.com  # For remote rollout processing

Force re-upload:

--force                       # Re-upload evaluator even if unchanged

See all options:

eval-protocol create rft --help

Advanced options

Weights & Biases integration

Track training metrics in W&B for deeper analysis:

eval-protocol create rft \
  --base-model accounts/fireworks/models/qwen3-4b \
  --wandb-project my-rft-experiments \
  --wandb-entity my-org

Set WANDB_API_KEY in your environment first.

Custom checkpoint frequency

Save intermediate checkpoints during training:

firectl rftj create \
  --base-model accounts/fireworks/models/qwen3-4b \
  --checkpoint-frequency 500  # Save every 500 steps
  ...

Available in firectl only.

Custom timeout

For evaluators that need more time:

firectl rftj create \
  --rollout-timeout 300  # 5 minutes per rollout
  ...

Default is 60 seconds. Increase for complex evaluations.

For other tuning parameters — rollout concurrency, chunk size, loss method, and more — see Parameter Tuning.

Examples

Fast experimentation (small model, 1 epoch):

eval-protocol create rft \
  --base-model accounts/fireworks/models/qwen3-4b \
  --output-model quick-test

High-quality training (more rollouts, higher temperature):

eval-protocol create rft \
  --base-model accounts/fireworks/models/qwen3-4b \
  --output-model high-quality-model \
  --n 8 \
  --temperature 1.0

Remote environment (for multi-turn agents):

eval-protocol create rft \
  --base-model accounts/fireworks/models/qwen3-4b \
  --remote-server-url https://your-agent.example.com \
  --output-model remote-agent

Multiple epochs with custom learning rate:

eval-protocol create rft \
  --base-model accounts/fireworks/models/qwen3-4b \
  --epochs 3 \
  --learning-rate 5e-5 \
  --output-model multi-epoch-model

Using `firectl` CLI (Alternative)

For users already familiar with Fireworks firectl, you can create RFT jobs directly:

firectl rftj create \
  --base-model accounts/fireworks/models/qwen3-4b \
  --dataset accounts/your-account/datasets/my-dataset \
  --evaluator accounts/your-account/evaluators/my-evaluator \
  --output-model my-finetuned-model

Differences from eval-protocol:

Requires fully qualified resource names (accounts/…)
Must manually upload evaluators and datasets first
More verbose but offers finer control
Same underlying API as eval-protocol

See firectl documentation for all options.

Next steps

Prerequisites & Validation

Review requirements, validation, and common errors

Monitor training

Track job progress, inspect rollouts, and debug issues

Parameter tuning

Learn how to adjust parameters for better results

Get Started

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Reference

Installation and setup

Common CLI options

Advanced options

Examples

Using `firectl` CLI (Alternative)

Next steps

Prerequisites & Validation

Monitor training

Parameter tuning

​Installation and setup

​Common CLI options

​Advanced options

​Examples

​Using firectl CLI (Alternative)

​Next steps

Prerequisites & Validation

Monitor training

Parameter tuning

Installation and setup

Common CLI options

Advanced options

Examples

Using `firectl` CLI (Alternative)

Next steps