ParaGen-Bench: A Benchmark for Neural Network Parameter Generation

Neural network parameter generation is a paradigm of producing the trained weights of a target network through a learned generator. It has emerged as a new direction for acquiring model parameters beyond hand-designed algorithms and backpropagation. However, the field still lacks a benchmark that enables comprehensive and standardized comparison across existing methods and provides a systematic understanding of the current state of development. To close this gap, we introduce ParaGen-Bench, the first benchmark for neural network parameter generation, covering image classification, text classification, image segmentation, reasoning, coding, multimodal understanding, and reinforcement learning. It comprises 44,000 trained checkpoints across 41 architectures and 29 datasets, each paired with 50 paraphrased prompts and structured metadata capturing training trajectories and hyperparameters. We conduct a comprehensive evaluation of ten representative methods spanning three paradigms: diffusion-based, hypernetwork-based, and large language model (LLM)-conditioned approaches. Results show that larger and more structurally complex target models exhibit greater performance gaps, parameter errors cause particularly severe downstream degradation in image segmentation and reinforcement learning, and gaps remain between generated and trained LoRA adapters across reasoning, coding, and multimodal tasks. Finally, we propose simple yet effective LLM-instructed parameter generation (LPG), a two-stage baseline that combines structure-aware weight tokenization with prompt-conditioned latent generation, taking a step toward practical prompt-conditioned weight generation.