problems/cross-model-learning-transfer.md

# Transferring what was learned between unequal models

What should be transferred between models with different capacities so that acquired knowledge remains useful?

Text, an agent-oriented language, coarse conceptual tokens, and adaptive parameters are considered as possible transfer surfaces. Shared token names or adapter shapes alone do not settle semantic compatibility.

Status: open problem. Formulation and comparisons: reconstruction. Development: active.

## Alternative representations

### Explanatory text

Affords: Broad interpretability and inspectable claims.

Limits or losses: Can omit tacit distinctions encoded in learned behavior.

### Structured conceptual transfer

Affords: Can preserve task-specific relations compactly.

Limits or losses: Needs a common interpretation or an explicit translator.

### Parameter-level transfer

Affords: Offers a direct intervention into computation.

Limits or losses: Requires compatible parameter meanings and integration checks.

## Tension

Communicating a statement and transferring a competence are different success criteria.

## Challenge

How can the receiving model demonstrate the transferred capability and identify what it did not receive?

This challenge is an editorial prompt, not a claim that the evidence already resolved it.

## Proposed next move

Choose a narrow skill and compare transfer formats under equal resource budgets and independent held-out tasks.

Status: new model-proposed experiment or extension; not a reported result.

## Related problems

- [Changing the grain of a conceptual token](adaptive-token-granularity.md) — proposes coarse-to-fine transfer (corpus_evidence). A proposed small-to-large learning transfer explicitly uses coarse units that can later be broken down.
- [Distributed specialization and integration](distributed-specialization.md) — enables a proposed collaboration (corpus_evidence). Distributed specialists require a way to transfer what was learned into another system.
- [When transferred learning affects governing policies](learning-and-policy-permeability.md) — questions what a transfer carries (corpus_evidence). A transfer may be proposed as knowledge exchange while potentially affecting governing commitments. This remains a proposed possibility.

## Evidence anchors

- basis-cross-model-learning-transfer: The proposal has smaller and larger models exchange what they learn through text or a different agent-oriented language. It also considers coarse units that are later decomposed, and explicitly links communication at the parameter level with possible changes in governing policies. [Editorial paraphrase; presence of a proposal does not verify its truth.]

Root evidence and private recovery mappings are not part of this public release.

[Open in the reader](../index.html#node=cross-model-learning-transfer) · [All problems](../CATALOG.md)

## Research editions

- [An Evolving Semantic Representation Language](../documents/evolving-semantic-representation-language.md) (reconstruction).
- [Memory, Tokens, and Learning Geometry](../documents/memory-tokens-and-learning-geometry.md) (reconstruction).