Neon

Neon: Negative Extrapolation From Self-Training Improves Image Generation

This repository contains the official implementation for the paper Neon: Negative Extrapolation From Self-Training Improves Image Generation.

Abstract

Scaling generative AI models is bottlenecked by the scarcity of high-quality training data. The ease of synthesizing from a generative model suggests using (unverified) synthetic data to augment a limited corpus of real data for the purpose of fine-tuning in the hope of improving performance. Unfortunately, however, the resulting positive feedback loop leads to model autophagy disorder (MAD, aka model collapse) that results in a rapid degradation in sample quality and/or diversity. In this paper, we introduce Neon (for Negative Extrapolation frOm self-traiNing), a new learning method that turns the degradation from self-training into a powerful signal for self-improvement. Given a base model, Neon first fine-tunes it on its own self-synthesized data but then, counterintuitively, reverses its gradient updates to extrapolate away from the degraded weights. We prove that Neon works because typical inference samplers that favor high-probability regions create a predictable anti-alignment between the synthetic and real data population gradients, which negative extrapolation corrects to better align the model with the true data distribution. Neon is remarkably easy to implement via a simple post-hoc merge that requires no new real data, works effectively with as few as 1k synthetic samples, and typically uses less than 1% additional training compute. We demonstrate Neon's universality across a range of architectures (diffusion, flow matching, autoregressive, and inductive moment matching models) and datasets (ImageNet, CIFAR-10, and FFHQ). In particular, on ImageNet 256x256, Neon elevates the xAR-L model to a new state-of-the-art FID of 1.02 with only 0.36% additional training compute.

Official Resources

• Paper: https://huggingface.co/papers/2510.03597
• GitHub Repository: https://github.com/VITA-Group/Neon

Method

Algorithm 1: Neon — Negative Extrapolation from Self-Training

In one line: sample with your usual inference to form a synthetic set $S$; briefly fine-tune the reference model on $S$ to get $\theta_s$; then reverse that update with a merge $\theta_{\text{neon}}=(1+w),\theta_r - w,\theta_s$ (small $w>0$), which cancels mode-seeking drift and improves FID.

Benchmark Performance

Model type	Dataset	Base model FID	Neon FID (paper)	Download model
xAR-L	ImageNet-256	1.28	1.02	Download
xAR-B	ImageNet-256	1.72	1.31	Download
VAR d16	ImageNet-256	3.30	2.01	Download
VAR d36	ImageNet-512	2.63	1.70	Download
EDM (cond.)	CIFAR-10 (32×32)	1.78	1.38	Download
EDM (uncond.)	CIFAR-10 (32×32)	1.98	1.38	Download
EDM	FFHQ-64×64	2.39	1.12	Download
IMM	ImageNet-256	1.99	1.46	Download

Quickstart & Evaluation

For environment setup, downloading pretrained models, and evaluation scripts (for FID/IS), please refer to the GitHub repository's Quickstart section.

Repository Map

Neon/
├── VAR/                # VAR baselines + eval scripts
├── xAR/                # xAR baselines + eval scripts (uses MAR VAE)
├── edm/                # EDM baselines + metrics/scripts
├── imm/                # IMM baselines + eval scripts
├── toy_appendix.ipynb  # 2D Gaussian toy example (diffusion & AR)
├── download_models.sh  # Grab all checkpoints + FID refs
├── environment.yml     # Reproducible env
└── checkpoints/, fid_stats/ (created by the script)

Citation

@article{alemohammadneon2025,
  title   = {Neon: Negative Extrapolation From Self-Training Improves Image Generation},
  author  = {Alemohammad, Sina and Wang, Zhangyang and Baraniuk, Richard G.},
  journal = {arXiv preprint arXiv:2510.03597},
  year    = {2025},
  url     = {https://arxiv.org/abs/2510.03597}
}

Contact

Questions? Reach out to Sina Alemohammad — [email protected].

Acknowledgments

This repository builds upon and thanks the following projects:

• VAR — Visual AutoRegressive Modeling
• xAR — Beyond Next-Token: Next-X Prediction
• IMM — Inductive Moment Matching
• EDM — Elucidating the Design Space of Diffusion Models
• MAR VAE (KL-16) tokenizer