---
slug: meta_learning
name: Meta-Learning
one_liner: "A second-order property of a learning process: the object being updated is not object-level knowledge but the update-GENERATING machinery itself — the learning rate, hypothesis space, inductive bias, and search operators — so that a system gets faster at learning new tasks rather than merely better at the current one."
type: prime
schema_version: 2
status: density_v2_draft
categories: [structural, cognitive, computational]
origin_domain: learning_theory
alternate_origin_domains: [machine_learning, cognitive_psychology, education, organizational_theory]
aliases: [learning_to_learn, learning_sets, second_order_learning, learning_how_to_learn, tunable_learning_algorithm]
related: []
solution_archetypes: []
review_flags:
- "OPERATOR-DERIVED (op-02 self-application, route R / reflexive) — the SECOND-ORDER prime obtained by applying `learning` to itself: improvement of the learning PROCESS rather than of object-level knowledge. The target of update is the update-generating machinery (learning rate, hypothesis space, inductive bias, search operators), not the task content. Discovered in the op-02 pilot + sweep 2026-07-03. CONVERGENCE ×2: reached independently from both `learning` and `transfer_of_learning`. STRONG hold (verified absent from corpus; established named construct — Harlow 1949; Thrun & Pratt 1998; modern ML meta-learning / MAML). STATUS: HOLD pending owner keep/cut."
- "Created 2026-07-03; STUB ONLY (frontmatter + Core Idea + Notes). SI/SF/similarity are PROPOSER ESTIMATES pending triangulation. Body sections (Structural Signature onward) NOT written."
- "Phase-C action: re-similarity-check vs `learning` (the input / parent) and `transfer_of_learning`, `metacognition`, `metaplasticity`, `learning_curve_effects`; confirm it is the genuine second-order lift (improvement of the learning ALGORITHM, selectable/updatable independently of any object-level task performance) and not merely `metacognition` (monitoring/knowing about cognition — epistemic, not the improvement itself), `metaplasticity` (a neural-mechanism instance), or a restatement of `transfer_of_learning` (applying learned CONTENT across contexts); draw a subsumption edge learning→meta_learning + boundary edges to metacognition / metaplasticity / transfer_of_learning at incorporation; write a v1 draft and sync the one_liner across v1/v2 per CLAUDE.md before merge."
substrate_independence:
  composite_substrate_independence: 4
  domain_breadth: 5
  structural_abstraction: 4
  transfer_evidence: 4
  reasoning: 'The bare structure — a learning process whose *update-generating machinery* (learning rate, hypothesis space, inductive bias, search operators) is itself the object of update, so that the system improves its capacity/speed of future learning rather than its performance on the current task — is a genuinely substrate-neutral second-order form. It recurs wherever a learning process can itself be tuned: learning sets and cross-task acceleration in animal/human learning, meta-learning and hyperparameter/architecture learning in machine learning, self-regulated learning and learning strategies in education, and the capacity to learn faster in organizational learning. These are structurally-unlike siblings sharing only the abstract engine, so domain_breadth scores 5. Structural abstraction is 4 because the core is a pure statement about a level-2 target (the learning algorithm) distinct from the level-1 target (this task''s content), requiring no cognitive or neural substance to hold; it stays just short of 5 because the framing still leans on the notion of a "task" and of learning being "useful," a mild evaluative residue. Transfer evidence is strong (canonical construct independently named in learning theory, ML, cognitive psychology, and education).'
similarity_to_nearest_existing_prime:
  slug: learning
  similarity: 0.58
structural_framed:
  label: structural
  aggregate: 0.35
  criteria: {vocab_travels: 0.7, evaluative_weight: 0.3, institutional_origin: 0.2, human_practice_bound: 0.3, import_vs_recognize: 0.4}
  rationale: 'Reads structural. The core claim — a learning process whose update-generating machinery is itself the target of update — is recognized rather than imported, and travels cleanly into machine-learning meta-learning, animal learning sets, and self-regulated learning in education without carrying any one domain''s vocabulary. The frame residue is light: "task," "learning rate," and "useful future learning" import a mild evaluative/pedagogical cast (keeping evaluative_weight and import_vs_recognize off the floor), but the underlying second-order structure — a distinction between object-level performance now and the disposition to learn faster later — is substrate-neutral. Comparable framing to its parent `learning`.'
phase_b_passes_completed: []
---
# Meta-Learning

## Core Idea

Meta-learning is a *second-order* property of a learning process: the object being updated is not object-level knowledge but the *update-generating machinery itself*. Base-level `learning` runs the loop experience → update → improved performance *within a fixed learning process*, improving how well the system does the current task. Meta-learning names the fact that the learning process has parameters of its own — the learning rate, the hypothesis space, the inductive bias, the search operators — and that these too can be updated, so that the system gets *faster at learning new tasks* rather than merely better at the present one. It is reached from `learning` by applying learning to itself: learning about, and improvement of, the learning process — the reflexive self-application of the input prime.

The load-bearing anatomy is a distinction that the base engine does not name: *object-level performance* (how well this task is done now) versus *second-order learning capacity* (the disposition to learn future tasks faster). These are logically independent — the machinery can be tuned for the future acceleration it enables even when it confers no immediate gain on the current task, and conversely a system that performs the current task well may sit in machinery that generalizes slowly to the next. The feature present at the meta level but absent at the base is precisely this decoupling: a single-episode learner has no vocabulary for "getting better at learning," because within one task the learning process is a fixed background, not a variable. Lift the update up one level and the background becomes the foreground — the learning algorithm is now the thing that is measured, updated, and retained, producing *learning sets* and cross-task acceleration that a single-episode learner cannot represent.

Read structurally, the same second-order shape appears wherever a learning process can itself be tuned, and the siblings are genuinely unlike. In *animal and human learning*, meta-learning is Harlow's *learning sets* — subjects that have solved many discrimination problems solve new ones almost immediately, having learned not the answer but the *strategy* for finding it. In *machine learning*, it is meta-learning proper — MAML, learned optimizers, hyperparameter and architecture learning — where the search process is optimized so that future search is more effective, not just so it scores well now. In *education*, it is self-regulated learning and learning strategies: building the capacity to acquire new material faster rather than mastering any one syllabus. In *organizational learning*, it is the deliberate building of the capacity to learn faster as an institution. Across these, the shared abstract engine is a learning loop whose generating machinery is itself a measurable, updatable target; the psychological case is one species of the genus, and the structurally-unlike siblings show the genus is the bare second-order structure.

## Notes

**Provenance.** Produced by operator 02 (self-application), route R (reflexive), as the second-order prime obtained by applying `learning` to itself — i.e. learning about, and improvement of, the learning process itself, where the object of update is the update-generating machinery (learning rate, hypothesis space, inductive bias, search operators) rather than object-level knowledge. Surfaced in the op-02 self-application pilot + sweep on 2026-07-03. Reached by *two-route convergence*: independently derived from both `learning` and `transfer_of_learning`. Established named construct: Harry Harlow 1949 ("The formation of learning sets"), Thrun & Pratt 1998 ("Learning to Learn"), and modern ML meta-learning / MAML. Claimed siblings: learning sets and cross-task acceleration in animal/human learning, meta-learning and hyperparameter/architecture learning in ML, self-regulated learning and learning strategies in education, and the capacity to learn faster in organizational learning. Verified absent from the corpus (no `learning_to_learn` / `meta_learning` slug). Manifest: STRONG hold.

**For owner evaluation (keep/cut).** The case FOR is strong: meta-learning is a *canonical, named construct* (not a proposer coinage), verified absent, with a clean second-order anatomy — the object of update is the *learning algorithm*, distinct from any object-level task content — reached by two independent routes and spanning psychology, ML, education, and organizational learning. It fills a real gap: a prime for "improvement of the learning process itself" that the base `learning` prime does not name. The RISKS Kurt should weigh: (1) **overlap with `metacognition`** — the discriminating claim is that metacognition is *monitoring/knowing about* one's cognition (an epistemic relation), whereas meta-learning is the *actual improvement of the learning algorithm* (a different structure: monitoring ≠ improving); they are distinct and should be separated by a boundary edge, not merged. (2) **overlap with `metaplasticity`** — that is a *neural-mechanism instance* of the general shape (plasticity of plasticity), a substrate-specific realization rather than the substrate-neutral prime; it should attach as an instance/sibling, not merge. (3) **overlap with `transfer_of_learning`** — meta-learning is the *improvement of learning capacity*; transfer is *applying learned content across contexts*; they co-occur (learning sets manifest as transfer) but are structurally distinct, and the two-route convergence is itself a signal they are close neighbors needing a clean boundary. (4) Screen also vs `learning_curve_effects`. Sharp keep/cut question: *is "improvement of the learning process itself" a distinct prime, or does it collapse into `metacognition`, `transfer_of_learning`, or plain `learning` over many tasks?* — the proposer's answer is that the update-target being the learning ALGORITHM, decoupled from object-level performance, is a level-2 feature none of those name.

**At incorporation.** Re-run similarity against `learning` (input / parent), `transfer_of_learning`, `metacognition`, `metaplasticity`, and `learning_curve_effects` to confirm no collapse. If kept: draw a subsumption edge `learning` → `meta_learning` and boundary edges to `metacognition` (monitoring ≠ improving), `metaplasticity` (mechanism/instance → prime), and `transfer_of_learning` (capacity improvement vs. content application), plus sibling edges to the learning-sets, ML-meta-learning, and self-regulated-learning instances (or the primes carrying them). Add a missing-edge note flagging the `metacognition` boundary and the `transfer_of_learning` two-route convergence explicitly. Write a v1 draft and sync the one_liner across v1 and v2 per CLAUDE.md before merge.
