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Learning, then using a model

Separate learned parameters from information supplied with a request.

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Scientific sources
Definition

Training adjusts parameters to reduce a loss or increase a reward. The objective, data and optimisation procedure shape what is learned. At inference, the model uses its parameters and available context to compute an output.

Equation

θ ← θ − η ∇θ L(θ)

θ denotes parameters, L a loss and η a learning rate. This gradient-descent step is illustrative; systems need not use exactly this update.

Assumptions and notation

θ denotes parameters, L a loss and η a learning rate. This gradient-descent step is illustrative; systems need not use exactly this update.

Limits of interpretation

Changing instructions changes the input; it does not necessarily retrain parameters. Supervised learning, self-supervised learning and reinforcement learning describe methods, not architectures.

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DefinitionEquationAssumptions and notationLimits of interpretationScientific sources
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Scientific sources

  • Vaswani et al. · 2017Attention Is All You NeedarXiv v7 · 2023
    Original publication
  • Hafner et al. · 2023Mastering Diverse Domains through World ModelsDreamerV3 · arXiv v2 · 2024
    Original publication

Teaching synthesis of the cited sources. Published results remain tied to their tasks, protocols and budgets.

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18 results

LearnLearning, then using a modelSeparate learned parameters from information supplied with a request.
LearnFrom context to the next tokenSeparate the model’s probabilities from the rule that selects an output.
LearnFrom text to representationsUnderstand why words become vectors without equating a vector with human meaning.
LearnWhat kind of memory?Separate working context, recurrent state, learned parameters and persistent storage.
LearnTransformers: understanding attentionCompare architectures, training methods and complete systems at the right level.
LearnGenerating by denoisingDistinguish corruption during learning from progressive generation.
LearnFrom a model to a system that actsUnderstand the harness around a model and its tools.
LearnPredicting to choose an actionExplore longer horizons and the risk of inaccurate predictions.
LearnDefinitions, not a single verdictExamine generality, performance and adaptation before applying a label.
LearnWhat can a result establish?Move from a convincing impression to a reproducible comparison.
ConfigurePrepare a ChatGPT projectA clear objective, useful context and instructions you can actually use.
ConfigureChoose for your taskStart with your needs. Compare access and outcomes without a universal model ranking.
ConfigureMake your request verifiableTurn an intention into an objective, context and success criteria.
ConfigurePut the right information in the right placeDistinguish working rules, task resources and memory managed by the product.
ConfigurePrepare a development environmentDefine the model’s role, harness capabilities and the access you actually need.
ConfigureCompose a development workflowOrganise preparation, changes, verification and reporting. Reuse the steps that help.
ConfigurePrepare a mission, then check the resultA reproducible example, a checklist and a record of your observations.
Public programsTentacularPublicly available uses, features and resources.