Preservation Is Not Enough for Width Growth: Regime-Sensitive Selection of Dense LM Warm Starts
Width expansion offers a practical route to reuse smaller causal-language-model checkpoints, but selecting a widened warm start is not solved by zero-step preservation alone. We study dense width growth as a candidate-selection problem over full training states, including copied weights, optimizer moments, and scheduler state. In a small-scale TinyStories proxy, we compare exact-copy, perturbative, asymmetric-reset, and structured non-clone warm starts under matched continuation budgets. We evaluate zero-step preservation, short-lag probe metrics, and downstream continuation utility in deterministic and stochastic regimes. The picture is mixed and partially replicated through a reduced-po...
| Authors | Eren Unlu |
|---|---|
| Primary category | cs.AI |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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