Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design--balancing syntactic reliability, performance, and structural novelty--remains underexplored. We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles. The model synthesizes PyTorch convolutional networks which are validated, evaluated via low-fidelity performance signals (single-epoch accuracy), and filtered using a MinHash-Jaccard criterion to prevent structural redundancy. High-performing, novel architectures are converted into prompt-code pairs for iterative fine-tuning via parameter-efficient LoRA adaptation, initialized from the LEMUR dataset. Across cycles, the LLM internalizes empirical architectural priors, becoming a robust generator. The valid generation rate stabilizes at 50.6 percent (peaking at 74.5 percent), while mean first-epoch accuracy rises from 28.06 percent to 50.99 percent, and the fraction of candidates exceeding 40 percent accuracy grows from 2.04 percent to 96.81 percent. Analyses confirm the model moves beyond replicating existing motifs, synthesizing 455 high-performing architectures absent from the original corpus. By grounding code synthesis in execution feedback, this work provides a scalable blueprint for transforming stochastic generators into autonomous, performance-driven neural designers, establishing that LLMs can internalize empirical, non-textual rewards to transcend their training data.

翻译：大型语言模型（LLM）在程序合成方面表现出色，但其在神经架构设计领域——平衡语法可靠性、性能与结构新颖性——的自主探索能力仍未得到充分研究。我们通过将面向代码的LLM置于闭环合成框架中，分析了其在22个监督微调周期内的演化过程。该模型合成了经过验证的PyTorch卷积网络，并通过低保真度性能信号（单轮准确率）进行评估，同时使用MinHash-Jaccard准则进行过滤以防止结构冗余。高性能且新颖的架构被转换为提示-代码对，通过基于LEMUR数据集初始化、参数高效的LoRA适配进行迭代微调。在多个周期中，LLM内化了经验性架构先验，成为稳健的生成器。有效生成率稳定在50.6%（峰值达74.5%），平均单轮准确率从28.06%上升至50.99%，准确率超过40%的候选架构比例从2.04%增长至96.81%。分析证实该模型超越了复制现有模式的阶段，合成了455个原始语料库中不存在的高性能架构。通过将代码合成建立在执行反馈的基础上，本研究为将随机生成器转化为自主的、性能驱动的神经设计器提供了可扩展的蓝图，并证明LLM能够内化经验性的非文本奖励，从而超越其训练数据。