Having explored the theoretical underpinnings of AI preference modeling in RLAIF, we now transition to the practical task of training a basic preference model. This model serves as the cornerstone for generating the reward signal used in the subsequent reinforcement learning phase. Our goal is to train a model capable of predicting which of two responses, $y_1$ or $y_2$ , is preferred for a given prompt $x$ , based on labels potentially generated by another AI (as discussed in generating-ai-preference-labels). This practice session focuses on the core mechanics of this supervised training process.

Preparing the Preference Dataset

The first step is structuring the data. RLAIF preference modeling requires a dataset where each sample consists of:

A prompt $x$ .
A pair of responses $(y_1, y_2)$ generated for that prompt.
A label indicating which response is preferred. Often, this is derived from an AI labeler, possibly guided by a constitution or other criteria.

For this exercise, we assume you have a dataset of triplets $(x, y_w, y_l)$ , where $y_w$ is the "winning" (preferred) response and $y_l$ is the "losing" (dispreferred) response according to your AI labeler.

A common representation uses Python dictionaries or pandas DataFrames:

# Example structure for a single data point
preference_data_point = {
    "prompt": "Explain the concept of quantum entanglement in simple terms.",
    "response_chosen": "Imagine two coins that are linked. If you flip one and it lands heads, you instantly know the other is tails, no matter how far apart they are. Quantum entanglement is like that for tiny particles.", # y_w
    "response_rejected": "Quantum entanglement involves the superposition principle and wave function collapse correlating particle states. It's a complex Hilbert space phenomenon described by Bell's theorem.", # y_l
}

# A small dataset might look like:
dataset = [
    {"prompt": "P1", "response_chosen": "PC1", "response_rejected": "PR1"},
    {"prompt": "P2", "response_chosen": "PC2", "response_rejected": "PR2"},
    # ... more data points
]

While we use synthetic AI labels here, remember that the quality and diversity of prompts and the consistency of the AI labeler significantly impact the final preference model's effectiveness. Ensure your prompts cover a wide range of expected use cases and potential failure modes.

Preference Model Architecture

The standard approach leverages a pre-trained language model as the backbone. The preference model needs to ingest the prompt $x$ and both responses $y_1, y_2$ to predict a preference score. A typical architecture computes a scalar score $r(x, y)$ for each response individually and then compares these scores.

The architecture usually involves:

Tokenization: Concatenate the prompt and a response (e.g., prompt + response_chosen) and tokenize them. Repeat for the other response.
Encoding: Pass the tokenized sequences through the LLM backbone (e.g., a GPT variant, Llama, etc.).
Scalar Head: Extract a representation (often the embedding of the last token or a pooled representation) and pass it through a linear layer (the "scalar head") to produce a single score $r(x, y)$ for each prompt-response pair.
Comparison: The preference between $y_1$ and $y_2$ is determined by comparing their respective scores, $r(x, y_1)$ and $r(x, y_2)$ .

The preference model typically processes prompt-response pairs through an LLM backbone and a scalar head to generate scores, which are then used to compute the preference loss.

Loss Function for Pairwise Preferences

The most common loss function for training preference models is derived from the Bradley-Terry model, which posits that the probability of $y_w$ being preferred over $y_l$ can be modeled using the difference in their underlying scores. We use a logistic function (sigmoid, $\sigma$ ) to map the score difference to a probability. The training objective is to maximize the log-likelihood of observing the preferences in the dataset. Minimizing the negative log-likelihood gives us the loss:

L = - \mathbb{E}_{(x, y_w, y_l) \sim D} [\log \sigma(r_\theta(x, y_w) - r_\theta(x, y_l))]

Here, $r_\theta(x, y)$ is the scalar score output by our preference model with parameters $\theta$ for prompt $x$ and response $y$ . $D$ represents the preference dataset. This loss encourages the model to assign a higher score to the chosen response ( $y_w$ ) than the rejected response ( $y_l$ ).

Training Loop Implementation Sketch

Let's outline the core training loop using a PyTorch-like pseudo-code structure, assuming you have a PreferenceModel class wrapping a pre-trained transformer and a scalar head, and a dataset providing batches of $(x, y_w, y_l)$ .

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, Dataset
from transformers import AutoModel, AutoTokenizer # Example imports

# Assume PreferenceModel exists, taking a backbone model name
# Assume PreferenceDataset is defined to handle tokenization and batching
# Assume dataset is an instance of PreferenceDataset

# Configuration
model_name = "path/to/your/base_llm" # Or HF identifier
learning_rate = 1e-5
batch_size = 4
num_epochs = 1

# Initialize Model, Tokenizer, Optimizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Add padding token if necessary
if tokenizer.pad_token is None:
    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
    # Resize model embeddings if needed

preference_model = PreferenceModel(model_name)
preference_model.model.resize_token_embeddings(len(tokenizer)) # Adjust for added tokens
optimizer = AdamW(preference_model.parameters(), lr=learning_rate)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Training Loop
preference_model.train()
for epoch in range(num_epochs):
    total_loss = 0
    for batch in dataloader:
        # batch contains tokenized prompts, chosen responses, rejected responses
        # Example keys: 'prompt_ids', 'chosen_ids', 'chosen_mask', 'rejected_ids', 'rejected_mask'
        
        # Ensure tensors are on the correct device (e.g., GPU)
        chosen_ids = batch['chosen_ids'].to(device)
        chosen_mask = batch['chosen_mask'].to(device)
        rejected_ids = batch['rejected_ids'].to(device)
        rejected_mask = batch['rejected_mask'].to(device)

        # Get scores from the preference model
        # The model should handle concatenating prompt and response internally
        # Or receive pre-concatenated inputs
        # Assuming the model returns scores for chosen and rejected responses
        scores_chosen = preference_model(input_ids=chosen_ids, attention_mask=chosen_mask)
        scores_rejected = preference_model(input_ids=rejected_ids, attention_mask=rejected_mask)

        # Calculate loss
        # Loss = -log_sigmoid(score_chosen - score_rejected)
        loss = -F.logsigmoid(scores_chosen - scores_rejected).mean()

        # Optimization step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_loss += loss.item()

    avg_loss = total_loss / len(dataloader)
    print(f"Epoch {epoch+1}, Average Loss: {avg_loss:.4f}")

# Save the trained preference model weights
# torch.save(preference_model.state_dict(), "preference_model_basic.pth")

Key Implementation Notes:

Tokenization: Careful handling of tokenization, padding, and attention masks is essential, especially ensuring consistency between the chosen and rejected response processing. Concatenate the prompt and response before tokenization for optimal context.
Model Forward Pass: The PreferenceModel needs to be structured to compute the scalar score $r(x, y)$ . This usually means passing the transformer output's relevant state (e.g., last token hidden state) through the linear head.
Gradient Accumulation: For large models or limited GPU memory, gradient accumulation across multiple smaller batches might be necessary before each optimizer step.
Mixed Precision: Utilize mixed-precision training (e.g., torch.cuda.amp) for significant speedups and memory savings, especially important for large backbone models.

Evaluating the Preference Model

Before deploying the preference model to generate rewards for RL, evaluate its performance on a held-out test set of preference pairs. The primary metric is accuracy: the percentage of pairs where the model correctly predicts the preferred response (i.e., assigns a higher score to $y_w$ than $y_l$ ).

\text{Accuracy} = \frac{1}{|D_{\text{test}}|} \sum_{(x, y_w, y_l) \in D_{\text{test}}} \mathbb{I}[r_\theta(x, y_w) > r_\theta(x, y_l)]

Where $\mathbb{I}[\cdot]$ is the indicator function.

High accuracy (e.g., >75-80%, depending on task difficulty and labeler quality) indicates the model has learned the preference patterns present in the AI-labeled data. Also monitor the loss curve for convergence and check for overfitting. Qualitative analysis, examining cases where the model disagrees with the labels, can reveal insights into the labeler's potential biases or inconsistencies that the model might be learning.

Expert-Level Considerations

Handling Ties: The basic formulation assumes a strict preference. If your AI labeler provides ties (equal preference), you might need to adjust the loss function or data processing (e.g., omitting ties, or modifying the loss to penalize large score differences for tied pairs).
Base Model Choice: The capability of the backbone LLM used for the preference model matters. A more capable base model can potentially learn more nuanced preferences but requires more computational resources. Often, the preference model is of similar size or slightly smaller than the policy model being trained via RLAIF.
Parameter-Efficient Fine-Tuning (PEFT): Techniques like LoRA (Low-Rank Adaptation) can be highly effective for training preference models. By inserting and training only small adapter layers, you can significantly reduce the computational cost and memory footprint compared to full fine-tuning, while often achieving comparable performance. This is particularly relevant given the large size of modern LLM backbones.
Calibration: Check if the magnitude of the score difference $(r(x, y_w) - r(x, y_l))$ correlates with the confidence or strength of the preference. A well-calibrated model is often desirable for stable RL training.

This practice session equipped you with the fundamental steps to train an AI preference model. You prepared data, defined an architecture, implemented the training loop with the appropriate loss function, and considered evaluation metrics. The resulting model, capturing the preferences embedded in your AI-labeled dataset, is now ready to serve as the reward function provider in the RLAIF PPO training phase, which we cover next.