This guide is for more technically proficient users who are comfortable working directly with command line tools and have some experience training or fine-tuning models.
If you want to join our private beta to gain early-access to our RFT platform, with deep integrations to our Environments Hub, and hands-on support from our research team, send us your details via this form.

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Overview

Reinforcement Fine-Tuning (RFT) optimizes a language model with feedback-driven rewards from RL environments. On Prime Intellect, you can run scalable RFT jobs using our compute, our prime-rl trainer, and access hundreds of pre-built environments from our Environments Hub, which all use verifiers.
  • prime-rl (GitHub) - Trainer for large-scale FSDP training
  • verifiers (GitHub) - Library of modular components for building RL environments
  • Environments Hub - Community hub hosting hundreds of RL environments that can be used, evaluated and forked for your RL run
This guide walks you through end-to-end:
  1. Deploy compute using our RFT image with pre-installed dependencies
  2. Validate machine setup
  3. Configure RFT run by setting up RL environment(s) and config files
  4. Launch RFT run

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1) Deploy compute

Go to the Deploy GPU page on Prime Intellect, and select the “Prime RL (RFT)” image. This image is preloaded with CUDA, Python/UV, prime-rl, verifiers, and the Prime CLI to minimize setup.
Deploy Prime RL Image
prime-rl requires at least 2 GPUs for training.
Select your desired GPU configuration (at least 2 GPUs), and click deploy. Once the instance is running, use the SSH command to connect to the instance. Then, once connected, prime-rl is installed in /workspace/prime-rl: 
cd /workspace/prime-rl

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2) Validate machine setup

  1. Check that environment uses Python 3.12
uv run python -V
  1. Check that flash-attn is installed
uv run python -c "import flash_attn"
  1. Check that you can run SFT trainer in debug model (this requires 1 GPU)
uv run sft @ configs/debug/sft.toml
  1. Check that you can run the RL trainer debug mode (this requires 1 GPU)
uv run trainer @ configs/debug/train.toml
  1. Check that you can run the orchestrator against an inference server (this requires 1 GPU)
uv run inference @ configs/debug/infer.toml

uv run orchestrator @ configs/debug/orch.toml
  1. Check that you can run a simple SFT warmup (this requires 1 GPU)
uv run sft @ configs/reverse_text/sft.toml
  1. Check that you can run a toy RL run (this requires 2 GPUs)
uv run rl \
  --trainer @ configs/reverse_text/train.toml \
  --orchestrator @ configs/reverse_text/orch.toml \
  --inference @ configs/reverse_text/infer.toml

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3) Configure RFT run

We support doing RL on environments that are built with verifiers (repo). You can find hundreds of community-contributed environments on the Environments Hub.
Not all environments on the Hub are vetted or tested by Prime Intellect. For a set of verified, training and RL-ready environments, visit the prime-environments repository.
To install an environment temporarily within prime-rl, do: 
# optional: for pushing environments to the hub or accessing private environments
uv run prime login

# install the environment
uv run prime env install custom-environment
To persist your environment installation in the package-wide pyproject.toml, do: 
uv add custom-environment --index https://hub.primeintellect.ai/username/custom-environment
For quick API-based evaluation post-installation, do: 
uv run vf-eval custom-environment # -h for config options; defaults to gpt-4.1-mini, 5 prompts, 3 rollouts each
For training:
  1. Create trainer/inference/orchestrator config files following the aforementioned examples
  2. Then set id = custom-environment in the [environment] section of your orchestrator config (along with any desired Environment-level args in [environment.args]).
There is an example of this in the configs/wordle directory.

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Additional setup

  1. If you want to log your runs to W&B (wandb), log in
uv run wandb login
# Or set `export WANDB_API_KEY=...`
  1. If you require gated/ private models or datasets from HuggingFace, log in
uv run huggingface-cli login
# Or set `export HF_TOKEN=...`
  1. We provide a convenient tmux layout script to start a run and view the logs for trainer, orchestrator, and inference. To start the session simply run
bash scripts/tmux.sh

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4) Launch RFT Run

The prime-rl stack has three cooperating components: inference, orchestrator, and trainer. For convenience, you can launch all via a single entrypoint. Once you have your config files set up, you can launch the run with: 
uv run rl \
  --trainer @ configs/<your-environment>/train.toml \
  --orchestrator @ configs/<your-environment>/orch.toml \
  --inference @ configs/<your-environment>/infer.toml \
  --wandb.project <your-project> \
  --wandb.name <your-run-name>
This will launch the run and log to W&B. You can find more details and training examples in the prime-rl README (GitHub), including:
  • Multi-Node training
  • Detailed W&B logging
  • Detailed checkpointing
  • Benchmarking

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Checkpointing and Resuming

Enable periodic checkpoints: 
uv run trainer @ /workspace/configs/<your-environment>/train.toml --ckpt.interval 10
Resume from step 10: 
uv run rl \
  --trainer @ /workspace/configs/<your-environment>/train.toml \
  --orchestrator @ /workspace/configs/<your-environment>/orch.toml \
  --inference @ /workspace/configs/<your-environment>/infer.toml \
  --ckpt.resume-step 10 \
  --trainer.monitor.wandb.id <your-wandb-run-id> \
  --orchestrator.monitor.wandb.id <your-wandb-run-id>
See prime-rl README for details on checkpoint layout and async semantics (GitHub).

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References

  • prime-rl: Trainer for large-scale FSDP training — GitHub
  • verifiers: Library of modular components for building RL environments — GitHub
  • Environments Hub: Community hub hosting hundreds of RL environments that can be used, evaluated and forked for your RL run