Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization

Layer-wise capacity in large language models is highly non-uniform: some layers contribute disproportionately to loss reduction, whereas others are nearly redundant.

Existing layer-scoring methods provide sensitivity estimates but do not give a principled rule for converting those estimates into allocation or pruning decisions under a global hardware budget.

We introduce a curvature-aware, MDL-inspired framework built around the layer gain $\zeta_k^2=g_k^\top\widetilde H_{kk}^{-1}g_k$.

This quantity equals twice the maximal decrease predicted by the regularized layer-restricted quadratic model and incorporates inverse local curvature; it is therefore a local surrogate for reducible risk, not a universal dominance claim over gradient-norm scores.

After normalizing the gains into scores $q_k$, we formulate two convex programs: one allocates expert slots under diminishing returns, and the other assigns layer-wise pruning ratios while protecting high-score layers.

Both continuous programs have unique globally optimal solutions characterized by one dual variable and computable in $O(K\log(1/\varepsilon))$ time by bisection.

We also prove a quadratic transfer-regret bound: when source and target score vectors differ by at most $\delta$, the target surrogate cost of the transferred decision is within $O(\delta^2)$ of the target optimum.

Experiments on Mistral-7B and Gemma-7B show clear allocation gains in some settings and competitive, though mixed, pruning performance.

The framework therefore replaces an empirical score-to-decision heuristic with a budget-feasible optimization procedure whose guarantees apply to the stated continuous surrogates.

Code is available on github repo - [TKAI-LAB-Mali/Curvature-Weighted-Capacity-Allocation](this https URL)