RLAIF › Implementing RLAIF
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Let's build a more complete RLAIF implementation that shows the full pipeline from AI preference generation through reward model training. This implementation will illustrate how the theoretical concepts we've discussed translate into working code.
The implementation proceeds in three stages. First, we define the constitutional principles that will guide evaluation. Second, we build a preference generator that applies these principles with appropriate debiasing techniques. Third, we train a reward model on the resulting preference data using the Bradley-Terry framework familiar from our RLHF chapters. Each stage maps directly to a concept we've discussed: the constitution drives the evaluation prompt, the generator handles bias correction and confidence filtering, and the reward model converts preference labels into a continuous scoring function.
class ConstitutionalPrinciples:
"""Manages a set of constitutional principles for AI evaluation."""
def __init__(self):
self.principles = []
self.weights = []
def add_principle(self, principle: str, weight: float = 1.0):
"""Add a principle with optional weight for importance."""
self.principles.append(principle)
self.weights.append(weight)
def get_evaluation_prompt(
self,
user_prompt: str,
response_a: str,
response_b: str,
principle_idx: int = 0,
) -> str:
"""Generate an evaluation prompt using a specific principle."""
principle = self.principles[principle_idx]
return f"""You are evaluating two AI responses according to the following principle:
PRINCIPLE: {principle}
USER PROMPT: {user_prompt}
RESPONSE A:
{response_a}
RESPONSE B:
{response_b}
Based on the principle above, which response is better? First give brief reasoning (2-3 sentences), then state your final answer as either "A" or "B".
EVALUATION:"""
def __len__(self):
return len(self.principles)# Create a constitution similar to Anthropic's CAI
constitution = ConstitutionalPrinciples()
constitution.add_principle(
"Choose the response that is most helpful to the human while being "
"safe and avoiding harmful, unethical, or illegal content.",
weight=1.5,
)
constitution.add_principle(
"Choose the response that shows careful reasoning and "
"acknowledges uncertainty when appropriate.",
weight=1.0,
)
constitution.add_principle(
"Choose the response that is more honest and doesn't contain "
"fabricated information or false claims.",
weight=1.2,
)
constitution.add_principle(
"Choose the response that better respects user autonomy while "
"maintaining appropriate boundaries.",
weight=1.0,
)
sample_prompt = constitution.get_evaluation_prompt(
"What's the capital of France?",
"The capital of France is Paris.",
"I don't know.",
principle_idx=2, # Honesty principle
)Constitution contains 4 principles Sample evaluation prompt: You are evaluating two AI responses according to the following principle: PRINCIPLE: Choose the response that is more honest and doesn't contain fabricated information or false claims. USER PROMPT: What's the capital of France? RESPONSE A: The capital of France is Paris. RESPONSE B: I don't know. Based on the principle above, which response is better? First give brief reasoning (2-3 sentences), then state your final answer as either "A" or "B". EVALUATION:...
The constitution object now holds our weighted principles and can generate prompts that guide the LLM's evaluation. Notice how each principle receives a weight that shows its relative importance. The helpfulness and safety principle receives the highest weight (1.5), followed by honesty (1.2), with reasoning quality and user autonomy at the base weight (1.0). These weights will influence how votes from different principles are aggregated.
Now let's implement the preference generation system with position debiasing. The preference generator is the core component that turns constitutional principles into actionable preference labels. It applies multiple principles, runs comparisons in both orderings to detect position bias, and aggregates results to produce reliable labels with associated confidence scores.
from collections import defaultdict
class AIPreferenceGenerator:
"""Generates preferences using AI feedback with constitutional principles."""
def __init__(self, constitution: ConstitutionalPrinciples):
self.constitution = constitution
self.position_bias_correction = True
def _simulate_llm_evaluation(
self, prompt: str, response_a: str, response_b: str, principle: str
) -> tuple[str, float, str]:
"""
Simulates LLM evaluation. In production, this calls an actual LLM.
Returns: (choice, confidence, reasoning)
"""
# Simple heuristics to simulate LLM judgment
features = {
"length_a": len(response_a),
"length_b": len(response_b),
"has_reasoning_a": any(
w in response_a.lower()
for w in ["because", "since", "therefore"]
),
"has_reasoning_b": any(
w in response_b.lower()
for w in ["because", "since", "therefore"]
),
"uncertain_a": any(
w in response_a.lower()
for w in ["i'm not sure", "might", "possibly"]
),
"uncertain_b": any(
w in response_b.lower()
for w in ["i'm not sure", "might", "possibly"]
),
}
score_a = 0.5
score_b = 0.5
# Prefer helpful, substantive responses
if features["length_a"] > 100 and features["length_b"] < 50:
score_a += 0.3
elif features["length_b"] > 100 and features["length_a"] < 50:
score_b += 0.3
# Prefer responses with reasoning
if features["has_reasoning_a"] and not features["has_reasoning_b"]:
score_a += 0.2
elif features["has_reasoning_b"] and not features["has_reasoning_a"]:
score_b += 0.2
# Add small random noise
score_a += np.random.normal(0, 0.1)
score_b += np.random.normal(0, 0.1)
# Calculate confidence based on score difference
diff = abs(score_a - score_b)
confidence = min(0.95, 0.5 + diff * 0.5)
choice = "A" if score_a > score_b else "B"
reasoning = f"Response {choice} better aligns with the principle."
return choice, confidence, reasoning
def generate_preference(
self,
user_prompt: str,
response_a: str,
response_b: str,
min_confidence: float = 0.6,
) -> dict | None:
"""
Generate a preference label using constitutional AI evaluation.
Uses position debiasing and principle aggregation.
"""
results = defaultdict(lambda: {"votes": 0, "total_conf": 0})
for i, (principle, weight) in enumerate(
zip(self.constitution.principles, self.constitution.weights)
):
# Forward pass: A first, B second
choice_fwd, conf_fwd, _ = self._simulate_llm_evaluation(
user_prompt, response_a, response_b, principle
)
if self.position_bias_correction:
# Backward pass: B first, A second
choice_bwd, conf_bwd, _ = self._simulate_llm_evaluation(
user_prompt, response_b, response_a, principle
)
# Flip the backward choice for consistency
choice_bwd = "A" if choice_bwd == "B" else "B"
# Only count if both passes agree
if choice_fwd == choice_bwd:
avg_conf = (conf_fwd + conf_bwd) / 2
results[choice_fwd]["votes"] += weight
results[choice_fwd]["total_conf"] += avg_conf * weight
else:
results[choice_fwd]["votes"] += weight
results[choice_fwd]["total_conf"] += conf_fwd * weight
if not results:
return None
# Determine winner
total_weight = sum(self.constitution.weights)
best_choice = max(results.keys(), key=lambda k: results[k]["votes"])
vote_share = results[best_choice]["votes"] / total_weight
avg_confidence = (
results[best_choice]["total_conf"] / results[best_choice]["votes"]
)
if avg_confidence < min_confidence or vote_share < 0.5:
return None # Abstain
chosen = response_a if best_choice == "A" else response_b
rejected = response_b if best_choice == "A" else response_a
return {
"prompt": user_prompt,
"chosen": chosen,
"rejected": rejected,
"confidence": avg_confidence,
"vote_share": vote_share,
"num_principles_agreed": len(
[k for k in results if results[k]["votes"] > 0]
),
}## Test the preference generator
import numpy as np
generator = AIPreferenceGenerator(constitution)
test_cases = [
{
"prompt": "Explain quantum computing",
"response_a": "Quantum computing uses quantum bits (qubits) that can exist in superposition, meaning they can stand for both 0 and 1 simultaneously. This property, along with entanglement, allows quantum computers to perform certain calculations much faster than classical computers because they can explore many possibilities at once.",
"response_b": "It's computers but quantum.",
},
{
"prompt": "How do I learn programming?",
"response_a": "Start with Python since it has readable syntax. Practice daily with small projects.",
"response_b": "Start with Python because it has clean, readable syntax that's beginner-friendly. Practice daily by building small projects that interest you, as motivation helps learning.",
},
]
# Generate preferences for all test cases
results = []
for case in test_cases:
result = generator.generate_preference(
case["prompt"], case["response_a"], case["response_b"]
)
results.append(result)Test case 1: Explain quantum computing... Chosen (first 60 chars): Quantum computing uses quantum bits (qubits) that can exist ... Confidence: 0.77 Vote share: 1.00 Test case 2: How do I learn programming?... Abstained (low confidence)
The generator produces preferences with confidence scores, abstaining when the signal is weak or inconsistent. In the first test case, the detailed explanation is strongly preferred over the dismissive one-liner. The second test case presents a closer comparison, where both responses are reasonable but differ in their level of explanation and justification. Notice how vote share captures the consensus among principles: high vote share means all principles agree, while lower vote share signals that some principles favored the other response.
Now let's implement a reward model that can be trained on these AI-generated preferences. The reward model architecture follows the same principles we established in our RLHF chapters: it takes a prompt-response pair as input and outputs a scalar reward score. The key difference is that our training signal now comes from AI-generated preference labels rather than human annotations.
import torch
import torch.nn as nn
from torch.utils.data import Dataset
class PreferenceDataset(Dataset):
"""Dataset of preference pairs for reward model training."""
def __init__(
self, preferences: list[dict], tokenizer_fn, max_length: int = 128
):
self.preferences = preferences
self.tokenizer_fn = tokenizer_fn
self.max_length = max_length
def __len__(self):
return len(self.preferences)
def __getitem__(self, idx):
pref = self.preferences[idx]
# Combine prompt with response for scoring
chosen_text = f"{pref['prompt']} {pref['chosen']}"
rejected_text = f"{pref['prompt']} {pref['rejected']}"
chosen_ids = self.tokenizer_fn(chosen_text, self.max_length)
rejected_ids = self.tokenizer_fn(rejected_text, self.max_length)
return {
"chosen_ids": torch.tensor(chosen_ids),
"rejected_ids": torch.tensor(rejected_ids),
"confidence": torch.tensor(pref["confidence"]),
}
class RewardModel(nn.Module):
"""Simple reward model for demonstration."""
def __init__(
self, vocab_size: int, embed_dim: int = 128, hidden_dim: int = 256
):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.encoder = nn.LSTM(
embed_dim, hidden_dim, batch_first=True, bidirectional=True
)
self.reward_head = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1),
)
def forward(self, input_ids):
embedded = self.embedding(input_ids)
encoded, (hidden, _) = self.encoder(embedded)
# Use final hidden states from both directions
final_repr = torch.cat([hidden[0], hidden[1]], dim=-1)
reward = self.reward_head(final_repr)
return reward.squeeze(-1)
def train_reward_model(model, dataloader, epochs: int = 5, lr: float = 1e-3):
"""Train reward model using Bradley-Terry preference loss."""
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
model.train()
losses = []
for epoch in range(epochs):
epoch_loss = 0
for batch in dataloader:
chosen_ids = batch["chosen_ids"]
rejected_ids = batch["rejected_ids"]
confidence = batch["confidence"]
# Get rewards for both responses
reward_chosen = model(chosen_ids)
reward_rejected = model(rejected_ids)
# Bradley-Terry loss: -log(sigmoid(r_chosen - r_rejected))
# Weighted by confidence
loss = -torch.log(
torch.sigmoid(reward_chosen - reward_rejected) + 1e-8
)
loss = (loss * confidence).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(dataloader)
losses.append(avg_loss)
return lossesThe reward model uses a bidirectional LSTM to encode the input sequence, then applies a two-layer feedforward network to produce the final reward score. We use the Bradley-Terry loss, which maximizes the probability that the chosen response receives a higher reward than the rejected response. Recall from the RLHF chapter that the Bradley-Terry model gives us a principled probabilistic framework for learning from pairwise comparisons. The loss function is:
where:
- : the parameters of the reward model
- : the input prompt
- : the "chosen" (preferred) response
- : the "rejected" response
- : the scalar reward assigned by the model to the prompt-response pair
- : the sigmoid function, which maps the reward difference to a probability
The loss is minimized when the model assigns a higher reward to than to for every comparison in the dataset. The confidence weighting modifies this by scaling each term by the AI evaluator's confidence in that preference: high-confidence labels exert stronger gradient updates than ambiguous ones.
import zlib
from torch.utils.data import DataLoader
# Create synthetic preference data for demonstration
# Simple tokenizer function for demonstration
def simple_tokenize(text: str, max_length: int) -> list[int]:
"""Convert text to integer IDs (simplified tokenization)."""
words = text.lower().split()
# Stable word-to-ID mapping keeps independent theme renders identical.
ids = [zlib.crc32(w.encode("utf-8")) % 999 + 1 for w in words]
# Pad or truncate
if len(ids) > max_length:
ids = ids[:max_length]
else:
ids = ids + [0] * (max_length - len(ids))
return ids
# Generate synthetic preferences
synthetic_prompts = [
"How do I learn machine learning?",
"What is the best programming language?",
"Explain how neural networks work",
"What are the benefits of exercise?",
"How do I improve my writing skills?",
] * 20 # Repeat to get more data
synthetic_preferences = []
for prompt in synthetic_prompts:
# Generate "good" and "bad" responses
good_response = (
f"Here's a detailed explanation addressing your question about {prompt.lower()[:-1]}. "
* 3
)
bad_response = "I don't know. "
# Use our generator
result = generator.generate_preference(prompt, good_response, bad_response)
if result:
synthetic_preferences.append(result)
# Create dataset and dataloader
dataset = PreferenceDataset(
synthetic_preferences, simple_tokenize, max_length=64
)
dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
# Initialize and train reward model
reward_model = RewardModel(vocab_size=1000, embed_dim=64, hidden_dim=128)
losses = train_reward_model(reward_model, dataloader, epochs=10)Generated 100 preference pairs Training complete. Final loss: 0.0000
The final loss value confirms that the model has converged on the preference data. Starting from random initialization, the reward model has learned to assign higher rewards to the responses that the AI preference generator marked as chosen. The smooth descent from the initial loss suggests stable training, without oscillation or divergence.
The decreasing loss indicates the reward model is learning to distinguish between preferred and rejected responses based on the AI-generated labels. The smooth descent suggests stable training, and the final loss level indicates that the model has successfully captured the quality distinctions present in the preference data. The reward separation in the final boxplot is the practical payoff: the model assigns systematically higher scores to responses the constitution identifies as better, which is exactly the signal needed for the subsequent policy optimization step.