RLHF (Reinforcement Learning from Human Feedback)

Definition

Reinforcement learning from human feedback is a training method that turns human preference into an optimization signal. People are shown two or more candidate outputs for the same prompt and asked which is better, or asked to score a single output against a rubric. Those judgments train a reward model — a separate model whose only job is to predict how a human would score an output. The policy model is then optimized against the reward model's score, usually with a reinforcement-learning algorithm and a penalty that keeps it from drifting too far from where it started. The result is a model that produces answers people prefer, not merely answers that are statistically likely.

The method became standard because it solves a problem supervised training cannot. Writing a demonstration of the ideal answer is expensive and often impossible; recognizing which of two answers is better is fast, and something people do reliably. Preference comparison converts a hard authoring task into an easier judging task. Variants have multiplied since — direct preference optimization and its relatives skip the explicit reward model and optimize on the preference pairs directly, and reinforcement learning from AI feedback substitutes a model for the human judge on parts of the pipeline — but all of them inherit the same dependency.

That dependency is the human feedback itself. A reward model cannot be better than the preferences it was fit to, and systematic bias in the judges becomes systematic bias in the policy. Preference labels drift toward length, fluency and confidence, and on technical material a judge who cannot evaluate the substance will reward whichever answer sounds more authoritative. This is why frontier work of this kind is increasingly done by domain specialists, why disagreement between judges is measured rather than averaged away, and why held-out preference sets are kept for evaluating the reward model itself.

How DeepenSkill approaches it

DeepenSkill supplies the judges, and treats their competence as a measurement rather than a claim. Everyone clears the same bar before starting: credentials verified, a standardized domain test passed, then a practical task scored against gold reference data. On live work the team measuring the experts is separate from the team running them — the platform rejects a validation report authored by the party that delivered the work — agreement statistics are reported with the method named, the item and rater counts stated and a 95% interval attached, and every judgment traces to a verified contributor, a task spec and a timestamp. See expert RLHF work, the demonstration side of post-training, and a sample verification bundle you can recompute yourself.

FAQ

• What does RLHF stand for?

  Reinforcement learning from human feedback. Humans compare or score model outputs, those judgments train a reward model, and the policy model is then optimized against the reward model's score.

• Why does RLHF need a reward model?

  Because human judgments are far too slow and expensive to consult at every optimization step. The reward model is trained once to predict what the human judges would have said, then queried in their place during training.

• What limits the quality of RLHF?

  The judges. A reward model cannot be better than the preferences it was fit to, so systematic bias in the people rating outputs becomes systematic bias in the trained model.