AITOPAI热报
Papers

无梯度下降，将知识直接写入Transformer块的新方法

Stanford AI Lab@StanfordAILab
Post
View the post
Why we picked it

斯坦福团队搞了个新招：不用梯度下降，直接把事实写进Transformer的MLP里，论文被COLM 2026收了，想了解怎么不训练就灌知识的可以看看。

斯坦福团队提出一种无需梯度下降即可将事实知识写入Transformer MLP的封闭形式方法。该方法可直接将事实编码进Transformer块，无需训练。

Body

相关论文已被COLM 2026接收。研究者包括Jerry Liu、Garctrob、Ronny Junkins等。

Original · Stanford AI Lab

This amazing team shows how to build knowledge directly into Transformer blocks **without gradient descent**! Jerry Liu @jerrywliu MLPs store facts in language models. Can we write them into Transformers without training? New work w/ amazing team @garctrob @ronnygjunkins @EyubogluSabri , Atri Rudra & @HazyResearch gives a ✨closed-form✨ recipe for fact-storing, Transformer-ready MLPs. Accepted at COLM 2026! 🔗 View Quoted Tweet📊 1 ⚡

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include Transformer, MLP, 知识注入, 无梯度下降.

Source attribution: curated from Stanford AI Lab (@StanfordAILab).

#Transformer#MLP#知识注入#无梯度下降#COLM 2026

Keep reading

Recent stories with the same tag or section
PapersBuildersOfficialSelected02:40
Apple Research Links Gating to In-Context Learning in State Space Models

The non-obvious takeaway is that SSM weaknesses on retrieval may be fixable through gating design rather than requiring a wholesale return to Transformers. Model architects and ML engineers evaluating efficient alternatives to attention should study the paper's analysis to guide architecture choices. Compared with prior empirical comparisons between SSMs and Transformers, this work offers a mechanistic explanation rather than just benchmark observations, which is useful for anyone building long-context inference systems.

#SSM#gating#in-context learning#Transformers
Primary sourceApple ML ResearchSource
PapersBuilders10:44
Chinese content研究揭示 Transformer 组合泛化评估中的等价性与模块覆盖问题

这项研究解释了为什么不同论文测组合泛化结论互相矛盾：很多基准里的模块序列其实功能等价，模型是在走捷径。做基准设计或评估模型泛化能力的开发者应该看看，结论会直接影响你怎么切训练测试集。

#Transformer#组合泛化#Llama3-8B#LiveCodeBench
arXiv cs.LG@Purva Pruthi et al. (4)Source
PapersBuildersMulti-sourceSelected15:33
Sutton clarifies the Bitter Lesson: brute compute alone is not enough

The non-obvious takeaway is that Sutton himself rejects the compute-only reading of his essay, giving ammunition to researchers arguing that architectural innovation remains essential alongside scaling. Builders choosing between pouring budget into compute versus exploring new learning mechanisms should find the hardcoding-versus-architecture distinction useful as a design lens. The thread also grounds the debate with concrete examples, from autonomous driving rule systems to the Transformer, making it a crisp reference in scaling-versus-algorithm arguments.

#Bitter Lesson#Rich Sutton#scaling#Transformer
Event
宝玉@doteySource