| # Transferring what was learned between unequal models |
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| What should be transferred between models with different capacities so that acquired knowledge remains useful? |
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| 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. |
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| Status: open problem. Formulation and comparisons: reconstruction. Development: active. |
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| ## Alternative representations |
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| ### Explanatory text |
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| Affords: Broad interpretability and inspectable claims. |
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| Limits or losses: Can omit tacit distinctions encoded in learned behavior. |
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| ### Structured conceptual transfer |
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| Affords: Can preserve task-specific relations compactly. |
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| Limits or losses: Needs a common interpretation or an explicit translator. |
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| ### Parameter-level transfer |
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| Affords: Offers a direct intervention into computation. |
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| Limits or losses: Requires compatible parameter meanings and integration checks. |
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| ## Tension |
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| Communicating a statement and transferring a competence are different success criteria. |
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| ## Challenge |
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| How can the receiving model demonstrate the transferred capability and identify what it did not receive? |
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| This challenge is an editorial prompt, not a claim that the evidence already resolved it. |
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| ## Proposed next move |
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| Choose a narrow skill and compare transfer formats under equal resource budgets and independent held-out tasks. |
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| Status: new model-proposed experiment or extension; not a reported result. |
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| ## Related problems |
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| - [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. |
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| ## Evidence anchors |
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| - 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.] |
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| Root evidence and private recovery mappings are not part of this public release. |
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| [Open in the reader](../index.html#node=cross-model-learning-transfer) · [All problems](../CATALOG.md) |
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| ## Research editions |
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| - [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). |
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