NVIDIA 证明 AI 学习无需反向传播：EGGROLL 扩展进化策略至十亿参数
这项研究打破了深度学习十年来的反向传播依赖，做大规模模型训练或非可微分任务（如强化学习、神经架构搜索）的团队可以直接关注 EGGROLL，它可能改变你构建模型的方式。
NVIDIA 与牛津大学联合发表论文，提出 EGGROLL 方法，将进化策略（Evolution Strategies）扩展到十亿参数模型，无需反向传播即可训练。
该方法用两个低秩矩阵替代密集随机扰动矩阵，大幅降低内存消耗，达到纯推理吞吐量的 91%。EGGROLL 支持数十万并行变异，可与不可微分组件配合，在推理任务上与 GRPO 竞争。
团队还训练了纯 8 位整数循环语言模型 EGG，非线性来自整数溢出裁剪，而非激活函数，预训练时种群规模超过百万。
NVIDIA just proved AI doesn't need backpropagation to learn anymore.
Training giant neural networks has meant one thing for a decade.
You run backpropagation, compute gradients, and update weights with calculus. A new paper from NVIDIA and Oxford breaks that assumption. EGGROLL scales Evolution Strategies to billion-parameter models.
The trick replaces dense random perturbation matrices with two skinny low-rank ones. This cuts memory and hits 91% of pure inference throughput. The practical upside changes what you can build: > No gradients or backprop required > Hundreds of thousands of parallel mutations > Works with non-differentiable components > Competitive with GRPO on reasoning tasks The team also built EGG, a recurrent language model trained from scratch.
It runs entirely on 8-bit integers. No floating-point math appears anywhere in the network. Nonlinearity comes from integer overflow clipping, not activation functions. Population sizes exceeded one million workers during pretraining.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include 进化策略, 反向传播, NVIDIA, 低秩矩阵.
Source attribution: curated from AlphaSignal (@AlphaSignalAI).