Jev glossary
Evaluation & TrainingReinforcement learning from human feedback
RLHF
The human-preference training method that turned pretrained models into chatbots, and the method TypeSafe deliberately did not use.
- Category
- Evaluation & Training
- Also known as
- Reinforcement learning from human feedback
- Reading time
- 1 min
- Related terms
- 5
- Directory entries
- 1
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
RLHF trains a model to produce responses people prefer. It powered InstructGPT and ChatGPT and was co-invented by Diogo Almeida, now a TypeSafe cofounder, but the primer argues preference optimization can reward sycophancy and confident-sounding hallucinations.
It remains a good fit for conversational models. TypeSafe's position is that production automation needs a different objective: constrained decisions and calibrated uncertainty.
Tagstraining
Related terms
Definitions that connect to this one.
RLCDTypeSafe's post-training method: reinforcement learning that optimizes for calibrated decisions and probabilities instead of generated text.Evaluation & TrainingRLVRTraining against automatically verifiable rewards, which produced strong reasoning models that are slower and more expensive.Evaluation & TrainingSycophancyThe tendency of preference-trained models to say what people want to hear, one of the reasons TypeSafe chose a calibration objective.Evaluation & TrainingMode droppingPreference optimization narrowing a model's output distribution toward a favored style and away from other valid outputs.Evaluation & TrainingTypeSafe AIThe company behind Jev and the System One API, including the official SDKs, cookbooks, console, and published workflow evals.Core Concepts
From the Jev directory
Directory entries that go deeper on this term.
Why TypeSafe trains decision models with RLCD instead of RLHF: calibrated probabilities where 0.2 outcomes happen about 20% of the time, and the case for machine-to-machine automation.
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