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Goals

  • Set up distributed RL training with Tinker
  • Configure an OpenReward environment for training
  • Monitor training progress with WandB
  • Train a model on the WhoDunIt environment

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Prerequisites

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Setup

Tinker is a flexible API for efficiently fine-tuning open source models with LoRA. In this tutorial, we’ll use it to train a language model on an OpenReward environment using reinforcement learning. First, clone the OpenReward cookbook repository and navigate to the Tinker training example: 
git clone https://github.com/OpenRewardAI/openreward-cookbook.git
cd openreward-cookbook/training/tinker
Install the required packages: 
pip install -r requirements.txt
Or using uv: 
uv pip install -r requirements.txt
Next, create a .env file with your API credentials: 
TINKER_API_KEY=your_tinker_key_here
OPENREWARD_API_KEY=your_openreward_key_here
WANDB_API_KEY=your_wandb_key_here

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Understanding the Training Pipeline

The training pipeline combines three services:
  • Tinker provides the distributed compute infrastructure for running training
  • OpenReward provides the environments and tasks for the agent to learn from
  • WandB tracks metrics, logs, and training progress
As training runs, Tinker will sample rollouts from your OpenReward environment, compute rewards, and update the model using reinforcement learning. All metrics are logged to WandB for monitoring.

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Selecting an Environment

Browse available environments at OpenReward: Environment selection Let’s use the GeneralReasoning/WhoDunIt environment for this tutorial. This environment challenges agents to solve mystery scenarios. Environment selection Click the copy button to copy the identifier GeneralReasoning/WhoDunIt for use in your config.

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Configuration

Now we’ll update the configuration file for the training run. Open tinker-config.yaml and update the environment configuration to use GeneralReasoning/WhoDunIt: 
...
# Environment Configuration
environments:
  GeneralReasoning/WhoDunIt:
    splits:
      train:
        shuffle: true
        num_samples: null
    num_rollouts: 16
    max_failing_rollouts: 2
    temperature: 1.0
    reward_reduction: "sum"
    nonterminal_reward: 0.0
There are other settings you can modify here, including:
  • wandb_project_name: The WandB project where metrics will be logged
  • wandb_run_name: A name for this specific training run
  • model_name: The base model to fine-tune (using LoRA)
  • lora_rank: The rank for LoRA adapters (lower = fewer parameters)
  • batch_size: Number of samples per training batch
  • learning_rate: Learning rate for the optimizer
  • save_every: Save a checkpoint every N batches
  • num_rollouts: Number of rollouts to collect per environment per batch
  • temperature: Sampling temperature for the model (1.0 = default)

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Running Training

Now we can start training. The cookbook includes a main.py file with the complete training loop. Run training with: 
python main.py --config_path tinker-config.yaml
Training will begin and you’ll see output in your terminal: Environment selection The training process will:
  1. Load your model and initialize LoRA adapters
  2. Connect to Tinker’s infrastructure
  3. Sample rollouts from the WhoDunIt environment
  4. Compute rewards and update the model
  5. Log metrics to WandB
  6. Save checkpoints periodically

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Monitoring Training

Your training metrics will appear in your WandB dashboard. You can track rewards, response lengths and other key metrics in real-time. WANDB To view your WandB dashboard, go to https://wandb.ai/ and navigate to your project. You’ll see charts showing:
  • Training loss over time
  • Average reward per episode
  • Success rate on tasks
  • Learning rate schedule
Detailed rollout data is uploaded to your OpenReward runs page: Rollout list One rollout Local logs are saved to the path specified in your config (/tmp/tinker-rl in our example). These contain detailed information about each training step.

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Additional tips

Some environments require additional secrets, for example environments that use LLM graders or environments that use external search APIs. You can configure these additional secrets in the tinker-config.yaml: 
# secrets:
#   openai_api_key: "sk-different-key-for-this-env"

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Next Steps

Evaluate your model

Learn how to run evaluations on your trained model

Build your own environment

Create custom environments for training

Tinker Documentation

Learn more about Tinker’s capabilities