Scaling Closed-Loop Feature Channel Configuration with LLMs
LLMs can effectively optimize neural network channel configurations via closed-loop code generation and accuracy feedback, with performance improvements scaling in denser sampling regimes. The best model achieved 36.76% accuracy on CIFAR-100 using only 11.8M parameters, a significant improvement in parameter efficiency over earlier models that required 166.5M parameters for lower accuracy. Analysis of 462 verified candidates revealed architectural regularities, such as non-power-of-two channel w
Analysis
TL;DR
- LLMs can effectively optimize neural network channel configurations via closed-loop code generation and accuracy feedback, with performance improvements scaling in denser sampling regimes.
- The best model achieved 36.76% accuracy on CIFAR-100 using only 11.8M parameters, a significant improvement in parameter efficiency over earlier models that required 166.5M parameters for lower accuracy.
- Analysis of 462 verified candidates revealed architectural regularities, such as non-power-of-two channel widths occurring in 41.8% of cases and a preference for moderate early widths with expanded middle or later blocks in high-performing models.
Why It Matters
This research demonstrates that Large Language Models can serve as effective agents for automated neural architecture search (NAS), specifically for optimizing internal layer structures like channel widths. It provides empirical evidence that LLM-generated architectures can surpass human-designed or traditionally searched counterparts in terms of parameter efficiency, offering a new pathway for lightweight model design.
Technical Details
- Methodology: A closed-loop search process where LLMs generate executable code for neural networks, which are then evaluated for accuracy on CIFAR-100, providing feedback for subsequent iterations.
- Scale: The study scaled the search to 250 candidate networks per fine-tuning cycle, analyzing 2000 generated candidates across 8 cycles, resulting in 462 verified evaluations after filtering.
- Performance Trends: Per-cycle mean accuracy showed a positive linear trend (slope 9.87e-4, p=0.043). The high-performing frontier improved from 31.44% to 36.76% accuracy.
- Architectural Findings: High-performing models exhibited structured channel-allocation patterns, typically featuring moderate channel widths in early layers and expanded widths in middle or later blocks. Non-power-of-two widths were common (41.8%).
Industry Insight
- Efficiency Gains: Practitioners should consider LLM-driven NAS for scenarios requiring high parameter efficiency, as the results show substantial reductions in model size without sacrificing accuracy.
- Design Heuristics: The identified regularities (moderate early widths, expanded later blocks) provide actionable heuristics for manual architecture design or as constraints for future automated search algorithms.
- Scalability of LLM-NAS: The transfer of optimization signals to denser sampling regimes suggests that LLM-based architecture search is robust and scalable, warranting further investment in integrating LLMs into the model development pipeline.
Disclaimer: The above content is generated by AI and is for reference only.