Super Weights in LLMs and the Failure of Selective Training
Score
77挑战了超级权重可训练性的普遍直觉，提供了反直觉的实证结论。
实验设计严谨，包含多组对比和种子消融，论证充分。
为高效微调方法设计提供重要指导，警示避免孤立参数训练。
最新上传的预印本，紧跟大模型训练前沿研究。
arXiv预印本，作者机构未明，但实验可复现。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
Complete experimental design with rich control groups, counterintuitive conclusions with practical implications.
AI commentary
「This paper delivers a much-needed wake-up call for those obsessed with parameter importance pruning and selective training: what is important is not necessarily trainable, and success in fine-tuning lies in structured decompositions rather than targeting individual extraordinary parameters. For researchers designing low-cost fine-tuning methods, the controlled experiments (random vs. Super Weight vs. LoRA) are highly informative, and they reaffirm LoRA's robustness in data efficiency.」
Quick overview
Recent work identified 'Super Weights' in LLMs—individual parameters whose removal degrades model performance by orders of magnitude. This paper shows that such degradation is not universal across all LLMs. Additionally, training Super Weights in isolation (100–8,192 parameters) drops accuracy to random-guessing levels on OLMo-1B and OLMo-7B, and expanding to local neighborhoods of up to 36K parameters provides no improvement. In contrast, training an equal number of randomly chosen positions in the same layers improves performance. Standard LoRA, updating all attention weight matrix positions via low-rank structure with only 0.16% of parameters, succeeds. A 10-seed ablation confirms that constraining LoRA updates at Super Weight coordinates yields statistically indistinguishable results. These findings establish that parameter importance does not imply parameter trainability in isolation, and effective fine-tuning relies on structured decompositions over entire layers rather than targeting individually important weights.
- The performance degradation caused by pruning Super Weights does not universally apply to all LLMs.
- Fine-tuning Super Weights in isolation (100–8,192 parameters) drops accuracy to random-guessing on OLMo-1B and OLMo-7B.
- Standard LoRA succeeds with only 0.16% of parameters by updating all attention weight matrix positions.
- Parameter importance does not imply parameter trainability; effective fine-tuning relies on structured decompositions over entire layers.
Full text
This is an AI-generated Chinese digest; use "View original" for the source text.
Related items
85
FeaturedSetoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data
Setoka：面向异构数据个性化Agent的层级用户理解基准
This paper is well worth attention, especially for researchers and engineers working on personalized agents, dialogue systems, and memory-augmented AI. It not only provides a benchmark but defines levels of user understanding from psychology, revealing systematic failures of current systems at higher levels. If you are designing memory-based agents, this paper reminds you that memory systems need to support cross-temporal, cross-source abstract reasoning, not just retrieval.
85
FeaturedBAND: Bayesian Network Distribution Regression for High-Dimensional Distribution Estimation – Breaking the Curse
BAND：高维分布估计的贝叶斯网络回归方法——打破维度诅咒
This paper is a must-read for anyone working on high-dimensional statistics, probabilistic modeling, and generative AI. It provides a simple yet powerful theoretical answer: sparsity can break the curse of dimensionality, and Bayesian networks offer a natural framework. Compared to black-box methods like VAEs and GANs, BAND is interpretable and provably convergent, especially valuable for industry needs like reliable confidence intervals. If you research time series forecasting or risk management, this paper may change how you approach high-dimensional density estimation.
84
FeaturedCycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas
循环一致与不确定性感知的托卡马克边缘等离子体神经代理模型
This paper is worth attention because it demonstrates how advanced AI techniques (cycle consistency, uncertainty quantification) directly address a core computational bottleneck in nuclear fusion. For fusion researchers and engineers, it offers a practical tool to speed up simulations by orders of magnitude without sacrificing accuracy; for AI practitioners, it provides an excellent case of self-supervised learning and inverse problem solving in scientific applications.
83
FeaturedEquivariant Learning of a Transferable Three-Dimensional Classical Density Functional
等变学习可迁移的三维经典密度泛函
The most striking aspect of this paper is its direct coupling of machine learning to a core problem in statistical mechanics, using equivariant networks to learn a transferable density functional without any labels. For researchers in liquid physics or soft matter, this could be a new paradigm: do we need to run separate simulations for every new system anymore? For AI researchers, it also shows how to embed physical symmetries and variational principles into models rather than just fitting data. I recommend focusing on the methods, especially how equivariance and variational consistency are implemented—that's the real essence.
83
FeaturedComputational and Statistical Guarantees of the c-Rectified Flow
c-Rectified Flow 的计算与统计保证
This paper moves rectified flow from 'works in practice' to 'theoretically controllable,' which is especially important for those working on the underlying algorithms of generative models. If you are tuning pipelines like FLUX or SD3, understanding that ordinary rectified flow can fail when covariances do not commute may help you avoid hidden training pitfalls.
83
FeaturedLearning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
从高保真UMI数据学习可部署操作策略
The most striking point of this paper is proving that pure robot-free data can match real teleoperation performance, breaking the convention of needing a small real-robot anchor for post-training. For research groups looking to reduce hardware costs and industrial robot application developers, HiFi-UMI's data production system and open-source dataset are directly usable resources. Pay attention to the engineering details of head-mounted SLAM and microsecond synchronization—they are key to achieving the reported accuracy.
83
FeaturedCapital Markets LLM Reliability Score: From Plausible to Bankable
资本市场LLM可靠性评分：从“看似合理”到“可银行化”
This paper is valuable for both finance and AI communities: it provides an actionable, workflow-oriented evaluation framework that's closer to real needs than generic benchmarks. If you're deploying LLMs in fintech or responsible for explainability/auditability, this is a must-read. The finding on decision usefulness is particularly striking—judges agree it's critical, yet models vary the most on it.
83
FeaturedWhere Should Optimizer State Live? Tiered State Allocation for Memory-Efficient Mixture-of-Experts Training
优化器状态应放在哪里？面向内存高效MoE训练的分层状态分配
This paper is highly valuable for teams training MoE models: it demonstrates that with careful optimizer state allocation, one can dramatically reduce memory without sacrificing accuracy—and even improve performance. It is especially relevant for labs or enterprises constrained by 40 GB or 80 GB GPU memory. The tiered strategy and ablation on momentum retention are worth studying for optimizing training frameworks like DeepSpeed or Megatron.