Code Structure for an End-to-End RLHF System

Building a complete RLHF system involves managing multiple models, datasets, and training phases. A well-organized codebase is essential for reproducibility, maintainability, and efficient experimentation. As we integrate the SFT, Reward Modeling, and RL Fine-Tuning stages discussed previously, structuring the code effectively becomes a significant engineering task.

Let's consider a practical approach to organizing an end-to-end RLHF project. The goal is to create a modular structure where components can be developed, tested, and potentially swapped out independently.

Organizing Your RLHF Project

A typical RLHF project might involve the following main directories and files:

rlhf_project/
├── configs/                 # Configuration files (YAML, JSON)
│   ├── sft_config.yaml
│   ├── rm_config.yaml
│   └── ppo_config.yaml
├── data/                    # Datasets (raw, processed, preferences)
│   ├── sft/
│   ├── preferences/
│   └── prompts/
├── models/                  # Model checkpoints and related artifacts
│   ├── base_llm/            # (Optional) Local copy of base model
│   ├── sft_model/
│   ├── reward_model/
│   ├── ppo_policy_final/
│   └── ppo_checkpoints/
├── src/                     # Source code
│   ├── data_processing/     # Scripts/modules for data loading & preprocessing
│   │   ├── __init__.py
│   │   └── preference_dataset.py
│   ├── models/              # Model definitions (if customizing beyond libraries)
│   │   ├── __init__.py
│   │   └── reward_model.py
│   ├── training/            # Training loops and logic
│   │   ├── __init__.py
│   │   ├── sft_trainer.py
│   │   ├── rm_trainer.py
│   │   └── ppo_trainer.py   # Could leverage libraries like TRL
│   ├── evaluation/          # Evaluation scripts and metrics
│   │   ├── __init__.py
│   │   └── evaluate_alignment.py
│   └── utils/               # Shared utilities (logging, helpers)
│       ├── __init__.py
│       └── helpers.py
├── scripts/                 # Executable scripts to run stages
│   ├── run_sft.py
│   ├── run_rm.py
│   ├── run_ppo.py
│   └── run_evaluation.py
├── requirements.txt         # Project dependencies
└── README.md                # Project documentation

Key Components and Their Roles

1. Configuration (configs/): Centralize all hyperparameters, model names/paths, dataset paths, and training settings here. Using formats like YAML makes it easy to manage different experimental setups without changing the code. For instance, ppo_config.yaml would contain settings like learning rate, batch size, KL coefficient ($\beta$), PPO epochs, GAE parameters ($\lambda$, $\gamma$), model paths for the initial policy (SFT model) and the reward model.

2. Data Handling (data/, src/data_processing/): Store raw and processed datasets separately. The src/data_processing modules should handle loading data specific to each phase (SFT demonstrations, preference pairs, prompts for generation). Define clear data structures or classes (like PyTorch Dataset or TensorFlow tf.data.Dataset) for consistent handling.

3. Model Management (models/, src/models/): The models/ directory stores the actual model weights and tokenizer files. The src/models/ directory contains the code defining model architectures if you're customizing them (e.g., a specific head for the reward model on top of a base transformer). More often, you'll load models directly from libraries like Hugging Face Transformers, but managing checkpoints systematically is important.

4. Training Logic (src/training/): Encapsulate the training logic for each phase (SFT, RM, PPO) into separate modules or classes.

   • sft_trainer.py: Handles loading the base model, SFT data, and running the supervised fine-tuning loop.
   • rm_trainer.py: Loads the SFT model (or base model), preference data, defines the reward model architecture (often a classification head on the LLM), and implements the preference learning loss (e.g., Bradley-Terry).
   • ppo_trainer.py: This is often the most complex part. It orchestrates loading the SFT model (as the initial policy $\pi_{SFT}$), the reward model, setting up the PPO components (policy $\pi_{\theta}$, value function $V_{\phi}$, reference policy $\pi_{ref}$ often fixed as $\pi_{SFT}$), generating responses, scoring them with the RM, calculating advantages (using GAE), and performing the PPO updates including the KL penalty term: $$L_{PPO} = \mathbb{E}_t [\hat{A}_t \cdot \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon)] - c_1 \cdot L_{VF} + c_2 \cdot S[\pi_{\theta}] \\ \text{where } r_t(\theta) = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{old}}(a_t|s_t)} \\ \text{and the reward incorporates KL penalty: } R(s, a) = R_{RM}(s, a) - \beta \cdot \text{KL}(\pi_{\theta}(\cdot|s) || \pi_{ref}(\cdot|s))$$ Libraries like TRL (trl.PPOTrainer) abstract away much of this complexity, but understanding the underlying structure is beneficial.

5. Evaluation (src/evaluation/): Implement functions or scripts to evaluate models at different stages, particularly the final PPO-tuned policy. This includes calculating automatic metrics and preparing outputs for human evaluation.

6. Utilities (src/utils/): Place common helper functions, logging setups, argument parsing logic, etc., here to avoid code duplication.

7. Execution Scripts (scripts/): These are the entry points for running each part of the pipeline. They parse arguments, load configurations, instantiate the necessary trainer classes from src/training, and launch the training or evaluation process.

Data Flow and Interactions

The diagram below illustrates the high-level interaction between the main code components and data/model artifacts.

High-level structure of an RLHF project, showing interactions between configuration, data, executable scripts, source code modules (training, evaluation, data processing), and model artifacts.

Benefits of This Structure

• Modularity: Each stage (SFT, RM, PPO) is self-contained within its respective trainer module and execution script. This makes it easier to debug or modify one part without affecting others.
• Configuration Driven: Experiments can be easily configured and replicated by changing parameters in the configs/ directory.
• Clear Separation: Code (src/), data (data/), models (models/), configurations (configs/), and execution scripts (scripts/) are clearly separated, improving organization.
• Reusability: Utility functions (src/utils/) and data processing logic (src/data_processing/) can be shared across different stages.
• Scalability: While this structure works for single-machine development, it provides a foundation that can be adapted for distributed training environments by modifying the trainers and execution scripts.

Adopting a structured approach like this from the outset will save considerable effort as your RLHF system grows in complexity, allowing you to focus more on the algorithmic and modeling challenges rather than untangling disorganized code.