# Reinforcement Fine-Tuning **Entity class:** AI training method **Domain:** Artificial intelligence / Model training **Maturity:** Developed ## Definition Reinforcement fine-tuning adapts a pretrained model using reward signals tied to task outcomes, preferences, or evaluators. ## Mechanism and significance Its effectiveness depends on reward quality and resistance to reward hacking because the model will optimize the signal actually supplied rather than the intention behind it. ## Relationships - **Research dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] - **Ontology route:** [[ASI and RSI Timeline Ontology#Model Architecture and Compute Economics|Model Architecture and Compute Economics]] - **Primary fields:** [[Machine Intelligence]] · [[AI Infrastructure]] · [[Compute Race]] - **Adjacent concepts:** [[BERT Encoder]] · [[Embedding Model]] · [[Decision Model]] · [[System 1 Machine Intelligence]] ## Sources and provenance - [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — immediate source for this node's role in the broadcast research map. ## Evidence boundary The research dossier establishes why this entity or concept belongs in the Moonshots ontology. Time-sensitive organizational, product, policy, and performance claims should be checked against the linked primary source or a current authoritative source before reuse as settled fact.