Abstract
Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths can be optimized directly through executable code generation and accuracy feedback. However, those results were obtained from a relatively sparse set of valid evaluations, leaving open whether the observed optimization behavior transfers to a denser sampling regime and whether additional architectural regularities emerge when more generated networks are evaluated.
To test this, the same search setting is scaled to 250 candidate networks per fine-tuning cycle.
Analysis
The analysis covers 2000 generated candidates from 8 complete cycles, yielding 462 verified CIFAR-100 evaluations after task and metadata filtering. Per-cycle mean accuracy exhibits a positive linear trend with a slope of 9.87e-4 (p=0.043), while the high-performing frontier improves more strongly: the best observed accuracy increases from 0.3144 to 0.3676, and both the top-5 and top-10 cycle-level means exhibit positive trends.
The scaled run also reveals improved parameter efficiency. The best model reaches 0.3676 with 11.8M parameters, compared to an early high-performing model at 0.3144 with 166.5M parameters.
Beyond accuracy, the larger sample exposes architectural regularities that were difficult to assess from sparse observations. Non-power-of-two channel widths occur in 41.8% of verified candidates, and the strongest models share structured channel-allocation patterns characterized by moderate early widths and expanded middle or later blocks. These findings indicate that the channel-search signal observed in the initial study transfers.
Blogger's Review: This study reveals the potential of large language models in channel configuration by scaling the candidate networks, highlighting the balance between higher parameter efficiency and accuracy. The findings underscore the importance of architectural regularities, providing significant guidance for future model designs.