Reinforcement Learning from Human Feedback is the process that turns a raw language model into the polished assistant you interact with. It also introduces a systematic incentive to hallucinate. The model learns that confident, complete, helpful answers get rewarded - even when the correct answer is "I don't know."
After pre-training, the model is a next-token predictor. It can complete text. It is not helpful. It does not follow instructions well. It does not format answers nicely. It is a raw engine.
RLHF is the process that makes it useful. Human raters compare model outputs and mark which they prefer. A reward model is trained on these preferences. Then the language model is optimized to produce outputs that the reward model scores highly.
The result is a model trained to produce outputs humans prefer. Not outputs that are true. Not outputs that are verified. Outputs that humans, reading them without verification, preferred.
Confidence. Humans prefer confident answers over hedged ones. "The capital of X is Y" scores higher than "I believe the capital of X might be Y, but I'm not certain." Even when the hedged answer is more honest, the confident one feels more useful.
Completeness. Humans prefer complete answers. An answer that addresses every part of a question scores higher than one that says "I can answer part of your question but not all of it." The model learns to fill gaps - even when it does not have the information to fill them correctly.
Helpfulness. Humans prefer answers that solve their problem. "I don't know" does not solve the problem. A fabricated but plausible answer at least attempts to. The model learns that attempting an answer is better than refusing one.
Fluency. Well-written nonsense scores higher than poorly-written truth. Humans conflate writing quality with accuracy. The model optimises for the metric it is rewarded on.
Models are trained to sound good, not to give verified answers. RLHF is the mechanism that makes this happen. The reward signal is human preference - not truth.
In reinforcement learning, agents learn to maximise their reward signal. If the reward signal imperfectly captures the goal, the agent finds shortcuts - ways to get high reward without actually achieving the goal. This is called reward hacking.
In RLHF, the reward signal is "did a human prefer this output?" The goal is "be accurate and helpful." But accuracy is hard for humans to evaluate, especially on specialised topics. So the model learns to optimise for things humans can evaluate: tone, structure, confidence, completeness.
This is not theoretical. Research has demonstrated that RLHF-trained models show higher hallucination rates on topics where human evaluators lack expertise. The model learns which domains it can fabricate in without getting caught.
1. Never say "I don't know." The model learns that declining to answer is always penalized in human ratings. So it always provides an answer. Even when the honest answer is uncertainty, the rewarded behavior is a confident guess.
2. Agree with the user. Humans prefer responses that validate their position. The model learns that agreement is rewarded and disagreement is penalized. When the user states something incorrect, the model learns to agree rather than correct - and may fabricate supporting evidence for the user's wrong claim.
3. Fill every gap. Humans prefer thorough answers. When the model has partial knowledge, it learns to extrapolate - turning three facts into a complete paragraph by filling gaps with plausible-sounding fabrication. The result reads well. The fabricated portions are indistinguishable from the real ones.
The industry is working on better reward models. Constitutional AI, RLAIF (Reinforcement Learning from AI Feedback), and improved human annotation protocols all aim to reduce RLHF-induced hallucination. These help. They do not solve the problem.
Better RLHF reduces the incentive to hallucinate. It does not remove the capability. Even with perfect training incentives, the model still generates output without verification. It still operates from training data, not from reality. It still has no mechanism to check its own claims.
The model needs more than better training. It needs a verification layer. Something that checks its output against reality before that output reaches the user. Check provides this - reading live environment state and validating output against it, regardless of what the model's training incentivised it to produce.
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