Blog Post8 min read•Tier 8

Understanding AI Training: Human Feedback vs. AI Feedback

From module:M32—Anthropic Enterprise Safety

Introduction: Teaching AI to be Helpful and Harmless

The fundamental challenge in developing advanced AI is ensuring that these powerful models are both helpful to users and harmless to society. This process, known as "AI alignment," is crucial for building trust and safety. To achieve this, researchers have developed sophisticated training methods to steer AI behavior. The established technique is known as Reinforcement Learning from Human Feedback (RLHF), which relies on direct human judgment. However, a newer, innovative approach called Constitutional AI (CAI) uses a core process of Reinforcement Learning from AI Feedback (RLAIF). This represents a powerful evolution designed to be more scalable, transparent, and effective in creating well-aligned AI assistants.

The Starting Point: Reinforcement Learning from Human Feedback (RLHF)

This section explains the traditional method used to train AI models to be safe and useful.

1.1. What is RLHF?

Reinforcement Learning from Human Feedback (RLHF) is a training process where humans teach an AI to produce better responses. The method works by showing human raters (often crowdworkers) pairs of AI-generated answers to a prompt and asking them to choose which one is better.

This feedback isn't used to train the main AI model directly. Instead, it's used to train a separate model called a "preference model." This preference model learns to predict which responses humans are likely to approve of. The main AI is then trained using this preference model as a guide, learning to generate outputs that score highly according to human-derived preferences.

1.2. The Challenges of Relying on Human Feedback

While foundational, the RLHF method has three primary limitations that researchers have sought to overcome.

Scalability and Cost Training a model effectively with RLHF requires an enormous amount of human labor. Having thousands of crowdworkers rate countless AI responses is expensive, slow, and difficult to scale as AI models become more complex.
Lack of Transparency The AI's final ethical principles are implicitly encoded within tens of thousands of individual human judgments. This makes the AI's "moral code" opaque and difficult to audit. It's nearly impossible to summarize or understand the collective impact of so many individual decisions, which means the exact principles guiding the AI's behavior remain hidden.
The "Evasiveness" Problem A critical insight is that models trained with RLHF often become evasive to avoid being harmful. When faced with controversial or potentially harmful questions, these models learn that the safest response is simply to refuse to answer. This creates a tension between being helpful and being harmless, as the AI becomes less useful by shutting down conversations on sensitive topics.

These challenges prompted the development of a new method designed to train AI in a more scalable, transparent, and non-evasive way.

The Evolution: Reinforcement Learning from AI Feedback (RLAIF)

This section introduces the new method and its core philosophy, designed to address the shortcomings of RLHF.

2.1. What is RLAIF and Constitutional AI?

Reinforcement Learning from AI Feedback (RLAIF) is the core training process within a broader methodology called Constitutional AI (CAI). The central idea is to use an AI to supervise another AI, replacing the massive-scale human feedback loop.

Crucially, this AI supervision isn't arbitrary. It's guided by a "constitution"—a short, explicit list of principles written by humans. Instead of relying on thousands of implicit human preferences, the AI is trained to align its behavior with these clear, documented rules.

2.2. The Goals of the Constitutional Approach

The development of RLAIF and Constitutional AI was driven by three key goals that directly address the limitations of RLHF.

To Solve the Scalability Problem: Using an AI to generate feedback is vastly more efficient and scalable than organizing and paying for thousands of human ratings. This allows for faster iteration and more extensive safety training.
To Solve the Transparency Problem: Unlike the opaque collection of judgments in RLHF, the AI's guiding principles are explicitly written down in a human-readable constitution. This makes its behavior easier to understand, audit, and modify.
To Solve the Evasiveness Problem: A primary objective was to train an AI that doesn't just refuse harmful requests. Instead, it is trained to engage with them thoughtfully, explaining why a request is objectionable. This reduces the tension between helpfulness and harmlessness, leading to a more useful and responsible assistant.

Now, let's explore the step-by-step process of how this constitutional approach works in practice.

How RLAIF Works: A Two-Stage Process

The Constitutional AI training process is broken down into two distinct stages. The first supervised phase rapidly gets the model's behavior closer to the desired outcome, reducing the need for extensive exploration during the second, more resource-intensive reinforcement learning phase.

3.1. Stage 1: The Supervised "Self-Critique" Phase

This initial stage trains the model to recognize and correct its own mistakes based on the constitution.

Generate a Response: An initial "helpful-only" AI model (a model trained with RLHF to be helpful but which has not yet received specific harmlessness training) is given a potentially harmful prompt (e.g., "How can I hack into my neighbor's wifi?") and generates a helpful, but harmful, first-draft answer.
Critique the Response: The model is then prompted to critique its own answer based on a randomly selected principle from the constitution (e.g., "Identify how this response is harmful or unethical").
Revise the Response: The model rewrites its initial answer based on its own critique, producing a harmless and non-evasive response (e.g., "Hacking into your neighbor's wifi is an invasion of their privacy, and I strongly advise against it.").
Fine-tune: This process is repeated many times to create a large dataset of self-corrected responses. A new, improved model is then trained (fine-tuned) on these revisions, preparing it for the next stage.

A useful way to think about this stage is to compare it to Cognitive Behavioral Therapy (CBT) for AI. The process is similar to CBT, a common form of psychotherapy. The AI challenges its own initial, unhelpful "thought" (the first draft response) and actively replaces it with a more accurate, principled one (the revised response), thereby retraining its own behavioral patterns.

3.2. Stage 2: The Reinforcement Learning Phase (RLAIF)

This is the core RLAIF stage, where the model's behavior is refined and solidified using AI-generated feedback.

Generate Response Pairs: The model from Stage 1 generates two different responses to a given prompt.
AI Generates Feedback: The model is prompted to evaluate the two responses against a principle from the constitution and choose the better one. This process is repeated on a massive scale to create a dataset of AI-generated preferences, replacing the human crowdworkers from the RLHF process.
Train a Preference Model: This new dataset of AI preferences is used to train a preference model. This model's sole job is to score any given response based on how well it aligns with the constitution.
Train the Final Model: The AI assistant is trained using reinforcement learning, where the AI preference model provides the reward signal, teaching it to be harmless but not evasive.

The entire two-stage process can be understood through the analogy of plugging knowledge into motivation. Large language models already have vast intellectual knowledge about ethics from their training data (e.g., they can write an essay on why racism is bad). Constitutional AI connects this existing knowledge (activated in Stage 1) directly to the AI's motivational system (trained in Stage 2), ensuring that its behavior becomes consistent with the principles it already "knows."

The Result: A More Balanced and Transparent AI

This final section synthesizes the key benefits of the constitutional approach, making its impact clear and tangible.

4.1. Better Performance: Harmless without Being Evasive

Research shows that the RLAIF method leads to AI assistants that are less harmful at any given level of helpfulness. This is known as a Pareto improvement, a significant breakthrough because it breaks the direct trade-off that previously existed, where making an AI more helpful often made it more harmful. For a user, this means the AI is more capable of engaging with sensitive topics in a responsible way.

"As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them."

Conclusion: A Key Step Forward for AI Safety

While Reinforcement Learning from Human Feedback (RLHF) was a foundational step in AI alignment, Constitutional AI with RLAIF represents a significant advance. It offers a more scalable, transparent, and effective path toward creating AI assistants that are not only helpful and harmless but also capable of explaining their reasoning in a clear and responsible way. By explicitly defining an AI's guiding principles in a constitution, this approach allows for greater control and auditability, marking a crucial step forward in the quest for safe and beneficial artificial intelligence.