Scaling Closed-Loop Feature Channel Configuration with LLMs

Validating LLM-Driven Architecture Search

The paper "Scaling Closed-Loop Feature Channel Configuration with LLMs" builds upon initial findings that demonstrated the potential of large language models to optimize neural network architectures. Specifically, it focuses on configuring feature channel widths by generating executable code and receiving accuracy feedback in a closed-loop system. The core objective of this expanded study was to ascertain whether the promising optimization behavior observed in a sparse evaluation setting would hold true and reveal further insights when scaled to a denser sampling regime.

By significantly increasing the search space to 250 candidate networks per fine-tuning cycle, the researchers were able to evaluate a much broader range of architectural variations. This rigorous scaling, encompassing 2000 generated candidates and 462 verified CIFAR-100 evaluations, provided a robust dataset to validate the efficacy of the LLM-based approach. The findings confirm that the channel-search signal, initially identified, not only transfers but strengthens under more extensive evaluation, underscoring the method's reliability for automated architecture optimization.

Efficiency Gains and Performance Frontiers

A key outcome of the scaled experiment is the clear demonstration of improved performance and remarkable parameter efficiency. The study observed a positive linear trend in per-cycle mean accuracy, with a statistically significant slope, indicating consistent improvement over time. More strikingly, the high-performing frontier of models showed substantial gains, with the best observed accuracy increasing from 0.3144 to 0.3676. This improvement was not isolated, as both the top-5 and top-10 cycle-level means also exhibited positive trends, suggesting a general uplift in the quality of generated architectures.

Perhaps the most compelling result is the enhanced parameter efficiency. The top-performing model, achieving 0.3676 accuracy, did so with a mere 11.8 million parameters. This stands in stark contrast to an earlier high-performing model from the initial study, which achieved a lower accuracy of 0.3144 but required a substantially larger 166.5 million parameters. This dramatic reduction in parameter count for superior performance highlights the LLM's ability to discover highly optimized and resource-efficient network designs, a critical factor for deploying AI models in real-world applications with computational constraints.

Uncovering Architectural Regularities

Beyond quantitative performance metrics, the larger sample size facilitated the discovery of previously elusive architectural regularities. With sparse observations, it was difficult to discern common patterns, but the scaled run provided enough data to reveal underlying design principles. One significant finding is the prevalence of non-power-of-two channel widths, occurring in 41.8% of verified candidates. This challenges conventional wisdom that often defaults to power-of-two dimensions for computational convenience, suggesting that more nuanced configurations can lead to better results.

Furthermore, the analysis exposed structured channel-allocation patterns among the strongest models. These high-performing networks consistently shared a characteristic design: moderate channel widths in their early layers, followed by expanded widths in the middle or later blocks. This insight provides valuable guidance for human designers and could inform future neural architecture search algorithms, moving beyond brute-force exploration to more intelligently guided design principles. These emergent regularities underscore the LLM's capacity not just to optimize, but to implicitly learn and encode effective architectural heuristics.