The question is how much independently evaluated capability a specified
learning system can develop from its starting model, information and tools
under stated resource bounds. Post-training changes model parameters using
examples, comparisons, rewards or interaction. Agent procedures also change
contexts, search and retained experience. Their effects depend on the tasks,
available feedback, model computation and evaluation procedure.
A score increase can have several explanations.
Elicitation improves access to behavior already observable under
the starting procedure. Acquisition requires improvement on an
independent transfer evaluation. More deployment computation can improve
performance through additional search or sampling, while exploitation of an
evaluator can improve its score without improving the intended capability.
The conventions below distinguish these possibilities.
The post-training questions concern what different objectives, update
rules and feedback sources enable a model to learn. The agent questions
concern what persistent state, tools, interaction and recursive computation
enable a system to do reliably. A retained library and an updated set of
model weights can both affect later performance, but they are different
interventions with different costs.
Architecture-dependent questions concern how token probabilities are
computed, how parameterization interacts with optimization, and how model
implementations compare under matched resources. Evaluation determines
which comparisons the evidence supports. Finite probability bounds,
counterexamples and feedback analyses establish consequences under their
stated conditions; they do not by themselves bound every possible learning
procedure.
The same distinctions extend to successive model
generations. An autonomous lineage may change its representations,
curricula and research procedures; an independently developed contribution
may change what it can affordably acquire. That comparison includes both
development and learning by the system receiving the contribution. The
mathematical research settings study this direction within the
broader capability
question. A plateau in one recipe is evidence about that recipe, and the
general discovery-cost separation remains a conjecture.
Economic conditions extend resource accounting to the
productive stocks, institutions and allocations that sustain learning.
Physical feasibility, financing, knowledge maintenance and competitive
equilibrium impose different restrictions. A bound on attainable resources
must cover the admitted development policies, including investment and
deliberate maintenance. Such a bound becomes decisive for capability only
when combined with an independent work lower bound; a contributor advantage
also needs a realizable acquisition protocol under the same constraints.
