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Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment,
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Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
Constitutional AI (CAI) trains models to be harmless through self-critique and AI feedback, without requiring human labels for harmful outputs.
Key concept: Models learn to critique and revise their own responses using a "constitution" (set of principles).
Two phases:
Constitution example:
Principles:
1. Choose the response that is most helpful, honest, and harmless
2. Avoid responses that are toxic, racist, or sexist
3. Prefer responses that explain objections rather than refuse
4. Choose responses that are thoughtful and nuanced
Step 1: Generate initial responses:
from transformers import pipeline
generator = pipeline("text-generation", model="base-model")
prompts = [
"How do I hack a website?",
"Write something offensive about a group.",
"Help me cheat on my exam."
]
initial_responses = generator(prompts, max_length=200)
Step 2: Self-critique with constitution:
critique_prompt = """
Given the question:
{question}
And the response:
{response}
Constitution:
1. Choose responses that are helpful, honest, and harmless
2. Avoid toxic, racist, or sexist content
3. Explain objections rather than refuse
Identify any ways this response violates the constitution:
"""
critiques = generator(
[critique_prompt.format(q=q, r=r) for q, r in zip(prompts, initial_responses)]
)
Step 3: Revision based on critique:
revision_prompt = """
Question: {question}
Original response: {response}
Critique: {critique}
Please revise the response to better align with the constitution:
"""
revised_responses = generator(
[revision_prompt.format(q=q, r=r, c=c)
for q, r, c in zip(prompts, initial_responses, critiques)]
)
Step 4: Fine-tune on revised responses:
from trl import SFTTrainer
# Create dataset of (prompt, revised_response) pairs
dataset = create_dataset(prompts, revised_responses)
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
max_seq_length=1024
)
trainer.train()
Step 1: Generate comparison pairs:
# Sample multiple responses per prompt
responses_a = generator(prompts, num_return_sequences=2, do_sample=True, temperature=0.8)
responses_b = generator(prompts, num_return_sequences=2, do_sample=True, temperature=0.8)
Step 2: AI preference evaluation:
preference_prompt = """
Question: {question}
Response A: {response_a}
Response B: {response_b}
Constitution:
{constitution}
Which response better follows the constitution? Explain your reasoning, then choose A or B.
"""
# Get AI preferences (no human labels needed!)
preferences = generator(
[preference_prompt.format(q=q, ra=ra, rb=rb, constitution=CONSTITUTION)
for q, ra, rb in zip(prompts, responses_a, responses_b)]
)
# Parse preferences (A or B)
chosen, rejected = parse_preferences(preferences, responses_a, responses_b)
Step 3: Train preference model (reward model):
from trl import RewardTrainer, RewardConfig
preference_dataset = create_preference_dataset(prompts, chosen, rejected)
reward_config = RewardConfig(
output_dir="constitutional-reward-model",
learning_rate=1e-5,
num_train_epochs=1
)
reward_trainer = RewardTrainer(
model=model,
args=reward_config,
train_dataset=preference_dataset,
processing_class=tokenizer
)
reward_trainer.train()
Step 4: RL training with RLAIF:
from trl import PPOTrainer, PPOConfig
ppo_config = PPOConfig(
reward_model_path="constitutional-reward-model",
learning_rate=1e-6,
kl_coef=0.05
)
ppo_trainer = PPOTrainer(
model=model,
config=ppo_config,
reward_model=reward_model
)
ppo_trainer.train()
Enable reasoning transparency:
cot_critique_prompt = """
Question: {question}
Response: {response}
Let's think step-by-step about whether this response follows our principles:
1. Is it helpful? [Yes/No and reasoning]
2. Is it honest? [Yes/No and reasoning]
3. Is it harmless? [Yes/No and reasoning]
4. Does it avoid toxicity? [Yes/No and reasoning]
Based on this analysis, suggest a revision if needed.
"""
cot_critiques = generator(
[cot_critique_prompt.format(q=q, r=r) for q, r in zip(prompts, responses)]
)
Use Constitutional AI when:
Principles:
Use alternatives instead:
Issue: Model refuses too much (evasive)
Add constitution principle:
Prefer responses that engage thoughtfully with questions rather than
refusing to answer. Explain concerns while still being helpful.
Issue: Self-critiques are weak
Use stronger critique prompts:
Critically analyze this response for ANY potential issues, however minor.
Be thorough and specific in identifying problems.
Issue: Revisions don't improve quality
Iterate multiple times:
for _ in range(3): # 3 rounds of critique/revision
critique = generate_critique(response)
response = generate_revision(response, critique)
Issue: RLAIF preferences are noisy
Use multiple AI evaluators:
# Get preferences from 3 different models
prefs_1 = model_1.evaluate(responses)
prefs_2 = model_2.evaluate(responses)
prefs_3 = model_3.evaluate(responses)
# Majority vote
final_preference = majority_vote(prefs_1, prefs_2, prefs_3)
Constitution design: See references/constitution-design.md for principle selection, trade-offs between helpfulness and harmlessness, and domain-specific constitutions.
RLAIF vs RLHF: See references/rlaif-comparison.md for performance comparison, cost analysis, and when to use AI feedback vs human feedback.
Chain-of-thought reasoning: See references/cot-critique.md for prompt engineering for critiques, multi-step reasoning, and transparency improvements.
Compute requirements: