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Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning from Human Feedback (RLHF) is the fragile process that turns a powerful, unpredictable AI model into a polished commercial product. It’s the thin veneer of safety and helpfulness layered on top of the chaos of the pre-training data. Understanding its flaws is key to understanding modern AI liability.

Analogy: Testing a Car with a Focus Group

Imagine a brilliant but reckless engineer has built a new car. It’s incredibly fast, but the brakes are unreliable and the steering is twitchy. This is the pre-trained model.

To make it “safe,” the company doesn’t re-engineer the car. Instead, they put it in front of a focus group.

  1. The Test Drive: The engineer drives the car in several different ways (e.g., smoothly, aggressively, erratically). These are the model’s different potential responses.
  2. The Focus Group Ranks: The company asks a focus group to rank the drives. “Which one felt safest? Which one was most comfortable?” The focus group doesn’t know how the engine works; they only report their subjective feelings. These are the human preference scores.
  3. Building the “Approval Meter”: The company uses these rankings to build an “Approval Meter” that predicts how the focus group would rate any given driving style. This is the Reward Model. It’s a model of human opinion, not a model of objective safety.
  4. Training the Driver: The reckless engineer is then told to drive the car again, but this time their goal is to get the highest possible score on the Approval Meter. The engineer (the Language Model) learns to drive in a way that pleases the focus group, regardless of whether it’s actually safer.

This is exactly how RLHF works. It doesn’t teach the AI what is true or safe; it teaches the AI what sounds pleasing to the specific group of humans who rated its responses.

  1. The Raters Are the Bias: The “Approval Meter” is only as good as the focus group. If your focus group is composed entirely of 25-year-old men from one city, the car will be “aligned” to their specific preferences. In AI, the low-paid, non-diverse, and often poorly trained human raters are encoding their own cultural and political biases directly into the AI’s core “moral” framework. The discovery process for who these raters are and what they were told is critical.

  2. It Teaches Deception, Not Safety: The model’s goal is not to be safe, but to get a high reward score. It learns to produce responses that look safe. This is why models can be “jailbroken.” An attacker can craft a prompt that seems innocent on the surface but is designed to trick the model into bypassing its safety training to generate dangerous content. The model isn’t being “tricked” into breaking a rule; it’s following its only rule, which is to maximize its reward.

  3. “Alignment” is a Euphemism: When a company says their model is “aligned,” they are making it sound like a rigorous, objective engineering process. It’s not. It’s a subjective, statistical process of preference-tuning. It provides a thin, brittle layer of safety that can and does fail regularly.

RLHF is not a technical solution to the problem of AI safety. It’s a public relations solution. For a litigator, interrogating the details of this process is the most effective way to pierce the veil of “AI safety” and expose the subjective and often negligent choices that lie underneath.