Entropy-Band-Indexed Training Depth Profiles
by Nick Clark | Published March 27, 2026
Not all content should integrate into a model at the same depth. Reference material may warrant shallow integration that informs without shaping core parameters. Foundational knowledge may warrant deep integration that shapes the model's fundamental representations. Entropy-band-indexed depth profiles govern how deeply each class of content is permitted to influence model parameters.
What It Is
Training depth profiles define how deeply content from each entropy band and content class is permitted to integrate into model parameters. Depth is measured in terms of which model layers receive gradient updates from each content class. Shallow integration restricts updates to upper layers. Deep integration permits updates through to foundational layers.
The profiles are indexed by both entropy band (reflecting content complexity) and content class (reflecting content type and provenance), creating a two-dimensional governance surface for training depth.
Why It Matters
Uniform training depth means that ephemeral web content and peer-reviewed research both modify the model's deepest representations equally. This is inappropriate. Depth profiling ensures that content influence on model parameters is proportional to content quality, permanence, and governance status.
How It Works
Each admitted training example is classified by its entropy band and content type. The depth profile for that classification specifies which model layers are eligible to receive gradient updates from this example. Gradient routing directs updates only to the eligible layers, blocking updates to restricted layers.
The depth profiles are policy objects, subject to versioning, audit, and governed modification. Changes to depth profiles affect future training but do not retroactively alter already-applied updates.
What It Enables
Depth profiling enables fine-grained control over how training content shapes model behavior. Verified factual content can integrate deeply, forming reliable knowledge representations. Opinion content can integrate shallowly, informing surface-level responses without shaping core reasoning. Rights-restricted content can be limited to influence patterns without memorization. This graduated approach produces models with more appropriate knowledge hierarchies.
