RLHF: Stabilizing Behavior with Preferences (Alignment as Control)

Instruction tuning gives you an assistant.

RLHF is what makes that assistant feel stable.

Not perfect. Not safe by magic. Not truthful by default.

Stable.

If you’ve done any real reinforcement learning, RLHF should feel familiar:

• you define a reward signal
• you optimize behavior under that reward
• and you fight reward hacking for the rest of your life

The twist is: in RLHF, the reward is human preference — a learned proxy for “good answers.”

RLHF, Explained Like a Systems Engineer

Most explanations of RLHF start with the algorithm and end with “and it’s aligned now.”

That’s the wrong direction.

The right direction is: what problem is RLHF solving?

Two problems, really:

1. The assistant problem: make the model reliably follow instructions and social norms.
2. The stability problem: do that without destroying the model’s pretrained competence.

RLHF is a control loop that tries to balance those forces:

• push behavior toward “preferred answers”
• don’t drift so far that you lose general capability
• prevent the model from learning weird hacks that please the reward model

If you’ve ever tuned a production system, this should ring a bell:

It’s not “maximize a metric.”
It’s “maximize it under constraints, and detect when it starts lying.”

The RLHF Pipeline (Conceptually)

Here’s the standard three-stage mental model:

If you stop there, it sounds simple.

The engineering reality is in the words “reward model” and “stay near.”

The Reward Model: Turning Taste into a Signal

Preferences are not labels like “cat vs dog.” They’re judgments like:

• “This answer is more helpful.”
• “This answer is less toxic.”
• “This answer is clearer.”
• “This answer follows policy.”

So the typical setup is:

• generate multiple candidate answers
• ask humans to rank or pick the best
• train a model to score answers higher when they match those choices

That scoring model is the reward model.

And it inherits every classic risk of proxy metrics:

• it can be fooled by style
• it can overvalue verbosity
• it can reward “confident tone” over truth
• it can learn shallow shortcuts

The Leash: Why KL Penalties Matter More Than People Admit

If you aggressively optimize for reward, the model will drift. And drift can destroy competence fast.

So RLHF systems keep the model on a leash — typically by penalizing divergence from a reference model (often the SFT model).

In plain language:

• “Be more like the preferred answers…”
• “…but don’t become a different model entirely.”

This is the control knob.

Too loose:

• you get reward hacking and weird behaviors

Too tight:

• you get little improvement and lots of “safe but generic” responses

RLHF Failure Modes (And Why They Look Like RL)

If you’ve trained RL agents, you’ve seen these patterns:

• learn to exploit the reward function
• overfit to the training distribution
• become brittle to small environment changes
• optimize the wrong thing very efficiently

RLHF has analogs of all of them.

Alignment as Control: The Product Architecture Implication

Once you see RLHF as control engineering, you stop asking:

“Is the model aligned?”

And start asking:

“What are the control surfaces and how do we operate them?”

In practice, you will have multiple layers of alignment:

1. Model-level shaping (RLHF)
2. System policy (hard rules)
3. Tool authorization (what the model is allowed to do)
4. Retrieval constraints (what context it can see)
5. Output enforcement (validators, filters, redaction)
6. Human escalation (approval for high-stakes actions)

RLHF is a strong default — but it’s never the whole system.

Operating RLHF Models in Production

Even if you never train a model yourself, you still operate the outcome.

Your system needs to detect drift in behavior along the exact dimensions RLHF tries to control.

What to monitor (practically)

Ship-Ready Checklist: RLHF-Aware LLM Features

Resources

FAQ

What’s Next

By the end of June, we have the full model-side behavior story:

• Transformers made long-context language modeling scalable.
• Pretraining built broad capability.
• Instruction tuning created an assistant interface.
• RLHF stabilized behavior under preferences and constraints.

Now we can finally talk about the thing teams actually ship:

ChatGPT as a product architecture — context assembly, policy layers, tools, and observability around the model.

July: “Dissecting ChatGPT: The Product Architecture Around the Model”