AIAdvanced#Machine Learning#Safety
RLHF (Reinforcement Learning from Human Feedback)
R
Definition
A family of training methods that uses human demonstrations or preferences to train a reward signal and optimize a model toward that measured preference. It can improve behavior on evaluated tasks, but it does not establish truth, safety, or domain competence.
Source reviewed
- OpenAI: Aligning language models to follow instructions opens in a new tab
Supports: Documents the demonstration, comparison, reward-model, and optimization stages and its stated alignment limitations.
- OpenAI: Alignment research opens in a new tab
Supports: Explains that human-feedback methods depend on evaluators being able to judge the task and are not sufficient for general alignment.
Concept Diagram
Key Takeaways
1
RLHF optimizes a model toward measured human preferences in a defined training and evaluation setup
2
A reward model is a proxy for raters, not proof that an output is factual or safe
3
Performance must be tested on held-out tasks and populations relevant to the intended use
4
High-stakes workflows still need sources, checks, human accountability, and execution limits
Practical Example
A team asks raters to compare several support responses, trains a reward model on those comparisons, and evaluates the tuned model on held-out requests. For a finance workflow, the team still requires current primary sources and a human approval before any order, transfer, or user-facing claim.
Finished learning?
Related Terms
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Chain of Thought (CoT)A prompting pattern that asks a model to decompose a task into intermediate text. The text may help a person inspect a draft, but it is generated output, not proof of the model's internal process, factual accuracy, or a valid financial conclusion.
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Fine-TuningAdditional training of an existing model on a selected dataset for a defined task. It may change model behavior on evaluated examples; it does not prove domain accuracy, reduce factual errors by itself, or create a reliable trading system.
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Model CollapseDegradation that can occur when generative models are repeatedly trained on their own or other model-generated outputs, especially when the training process loses rare but important parts of the original data distribution.
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Prompt EngineeringThe practice of designing model instructions, examples, context, and output constraints for a bounded task. It can make a workflow easier to evaluate; it does not make model output deterministic, correct, secure, or suitable for a financial decision.
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