Reinforcement Learning from Human Feedback (RLHF) is a training technique that aligns LLM outputs with human preferences by training a reward model on human comparisons, then optimizing the LLM against that reward.
WHY IT MATTERS
RLHF is the technique that made LLMs useful. Raw pre-trained models are powerful but uncontrolled. RLHF aligns the model with human expectations of helpful, harmless, and honest behavior.
The process: human raters compare model outputs and indicate preferences. These preferences train a reward model. The LLM is then fine-tuned using reinforcement learning to maximize that reward — learning to produce outputs humans prefer.
RLHF is responsible for the dramatic difference between base models (unpredictable) and assistant models (helpful, instruction-following). It's also why different models have different 'personalities.'
Running agents against MCP servers? Route them through PolicyLayer and every tool call is checked against policy first.
Enforced before the call runs. Nothing to install.
FREQUENTLY ASKED QUESTIONS
What's the difference between RLHF and DPO?
RLHF trains a separate reward model then optimizes against it. DPO skips the reward model, directly training from preference pairs. DPO is simpler and increasingly popular.
Does RLHF make models safe?
It improves safety but doesn't guarantee it. Aligned models can still be jailbroken and make mistakes. RLHF is a training-time measure, not a runtime guarantee.
Can RLHF train agents to follow spending rules?
In theory, but it's too coarse for precise financial rules. Hard-coded policy enforcement is more reliable than learned preferences for financial constraints.
Route your MCP traffic through PolicyLayer. Every tool call is checked against your policy before it runs: allow, deny, or require approval. Per-identity grants. Full audit log. Live in minutes.