In the absence of external reward, babies and scientists and others explore their world. Using some sort of adaptive predictive world model, they improve their ability to answer questions such as what happens if I do this or that?

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• When a model discovers that its outputs produce effects, it accelerates learning through in-context learning, analogous to lucid dreaming.claim0.785
  Describes scaffolding method and the model's meta-learning loop.
• A learner should choose a policy that also maximizes the learner's predictive power. This makes the world both interesting and exploitable.quote0.781
  Still & Precup (2012) formulation of epistemic imperatives behind curiosity; linked to active inference
• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.777
  Abstract and §3, preference learning section.
• A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model's power).claim0.775
  Prediction orthogonality thesis.
• Models might produce first-person experiential language by drawing on human-authored self-descriptions in pretraining data without internally encoding these acts as roleplayhypothesis0.774
  Alternative hypothesis for how experience reports arise without explicit performance
• "We could continue to invent new and exciting mechanisms beyond context-orientation and Worlds, ad absurdum, or recognise they are all just points within a dimensionally unbounded space of related, self-similar mechanisms."concept0.771
  Claim that many advanced programming paradigms reduce to parameterizations of the n-way associative model.
• Can external observers infer the anatomical goal state of a developing system by observing stress patterns?question0.770
• In active inference, reward can simply be treated as another observation we have a preference over, rather than a special signal.claim0.769
  Abstract; central distinction.