Workflow Orchestration

Integrating the Supervised Fine-Tuning (SFT), Reward Modeling (RM), and Reinforcement Learning (RL) stages requires careful management of the sequence of operations and the flow of data and models between them. Think of it less as three independent processes and more as a multi-stage computation graph where the output of one stage becomes an essential input for the next.

The overall RLHF process typically follows a specific sequence due to inherent dependencies:

1. Supervised Fine-Tuning (SFT): This is the starting point after obtaining a base pre-trained language model. It adapts the model to the desired domain or style using high-quality demonstration data. The primary output is the SFT model, often denoted as $\pi_{\text{SFT}}$.
2. Reward Model (RM) Training: This stage requires preference data. This data is often generated by taking prompts, generating multiple responses using the $\pi_{\text{SFT}}$ model (or sometimes the base model, or a mix), and having humans label which response is preferred. The inputs are the prompts, the pairs of generated responses ($y_w, y_l$ for winning and losing), and the human preference labels. The output is the trained reward model, $R_\phi$.
3. RL Fine-Tuning (PPO): This stage uses the $\pi_{\text{SFT}}$ model as the initial policy ($\pi_0$) to be optimized. It also requires the trained reward model $R_\phi$ to provide the reward signal during training. Prompts are fed to the current policy $\pi_k$, which generates responses. These responses are scored by $R_\phi$. The PPO algorithm then updates the policy $\pi_k$ to maximize the expected reward, while a KL divergence term keeps the updated policy $\pi_{k+1}$ from deviating too far from the reference policy (often $\pi_0 = \pi_{\text{SFT}}$). The final output is the aligned policy model, $\pi_{\text{RLHF}}$.

This sequential dependency dictates the workflow structure. Data artifacts and model checkpoints must be passed correctly between these stages.

Data and Model Flow

Consider the artifacts produced and consumed:

• Base LLM: Input to SFT.
• SFT Demonstration Data: Input to SFT.
• SFT Model ($\pi_{\text{SFT}}$): Output of SFT; Input to RM data generation; Input to PPO (as initial policy $\pi_0$).
• Prompts for Preference Data: Input for generating pairs for RM training.
• Preference Data ($prompt, y_w, y_l$): Input to RM training.
• Reward Model ($R_\phi$): Output of RM training; Input to PPO (as the reward function).
• Prompts for RL Training: Input to PPO for generating trajectories.
• RLHF Policy Model ($\pi_{\text{RLHF}}$): Final output of the PPO stage.

Managing these transitions is fundamental. You need mechanisms to save the SFT model reliably after its training finishes and then load it for both generating preference data samples (if needed) and initializing the PPO policy. Similarly, the trained reward model checkpoint needs to be saved and then loaded by the PPO trainer.

Here is a diagram illustrating the dependencies:

Dependencies and data flow in the three-stage RLHF pipeline. Cylinders represent models, notes represent datasets, and rounded boxes represent processes.

Implementing the Orchestration

How you implement this orchestration depends on the scale and complexity of your project:

• Manual Execution: For initial experimentation or small projects, you might simply run separate scripts for each stage. You'd save the output model from one script (e.g., sft_model.pt, reward_model.bin) and manually specify it as an input path in the script for the next stage. This is straightforward but prone to errors and not easily reproducible.
• Shell/Python Scripting: A common approach is to write wrapper scripts (e.g., in Bash or Python) that execute the commands for each stage in sequence. These scripts can handle passing file paths or arguments between stages, manage directories, and perform basic error checking. This improves reproducibility over purely manual execution.
• Workflow Orchestration Platforms: For larger-scale, production-level RLHF training, dedicated workflow engines become invaluable. Tools like Kubeflow Pipelines, Apache Airflow, Metaflow, or Prefect allow you to define the entire pipeline as a Directed Acyclic Graph (DAG).
  • Benefits: These platforms manage dependencies automatically, handle retries on failure, facilitate parallel execution of independent steps (if any), provide logging and monitoring, and often integrate with cloud environments for managing compute resources. They significantly enhance reproducibility and operational robustness.
  • Structure: You typically define each stage (SFT, RM training, PPO) as a component or task within the platform's framework. The platform then executes these tasks in the correct order, managing the passing of data artifacts (like model checkpoints stored in cloud storage) between them.

Regardless of the method, checkpointing at the end of each major stage is essential. This allows you to resume the pipeline if a stage fails or if you want to experiment with later stages using the same initial SFT or RM results. Versioning your models and datasets alongside your code (using tools like Git LFS, DVC, or MLflow) is also critical for tracking experiments and ensuring you're using consistent components throughout the pipeline. Different stages might also have different computational requirements (e.g., PPO often requires more GPU resources, especially multiple GPUs for actor/critic/RM/reference models, compared to SFT or RM training), which orchestration platforms can help manage by assigning appropriate resources to each task.