Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Fuente:
arXiv
Saved in:
| Main Authors: | Aksenov, Yaroslav, Balagansky, Nikita, Vaina, Sofia Maria Lo Cicero, Shaposhnikov, Boris, Gorbatovski, Alexey, Gavrilov, Daniil |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Diffusion Language Models Generation Can Be Halted Early
by: Vaina, Sofia Maria Lo Cicero, et al.
Published: (2023)
by: Vaina, Sofia Maria Lo Cicero, et al.
Published: (2023)
Learn Your Reference Model for Real Good Alignment
by: Gorbatovski, Alexey, et al.
Published: (2024)
by: Gorbatovski, Alexey, et al.
Published: (2024)
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
by: Laptev, Daniil, et al.
Published: (2025)
by: Laptev, Daniil, et al.
Published: (2025)
You Do Not Fully Utilize Transformer's Representation Capacity
by: Gerasimov, Gleb, et al.
Published: (2025)
by: Gerasimov, Gleb, et al.
Published: (2025)
Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders
by: Kurochkin, Vadim, et al.
Published: (2025)
by: Kurochkin, Vadim, et al.
Published: (2025)
Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors
by: Sinii, Viacheslav, et al.
Published: (2025)
by: Sinii, Viacheslav, et al.
Published: (2025)
Teach Old SAEs New Domain Tricks with Boosting
by: Koriagin, Nikita, et al.
Published: (2025)
by: Koriagin, Nikita, et al.
Published: (2025)
Steering LLM Reasoning Through Bias-Only Adaptation
by: Sinii, Viacheslav, et al.
Published: (2025)
by: Sinii, Viacheslav, et al.
Published: (2025)
Trust-Region Behavior Blending for On-Policy Distillation
by: Plyusov, Daniil, et al.
Published: (2026)
by: Plyusov, Daniil, et al.
Published: (2026)
The Differences Between Direct Alignment Algorithms are a Blur
by: Gorbatovski, Alexey, et al.
Published: (2025)
by: Gorbatovski, Alexey, et al.
Published: (2025)
Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy
by: Balagansky, Nikita, et al.
Published: (2025)
by: Balagansky, Nikita, et al.
Published: (2025)
F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare
by: Plyusov, Daniil, et al.
Published: (2026)
by: Plyusov, Daniil, et al.
Published: (2026)
Mechanistic Permutability: Match Features Across Layers
by: Balagansky, Nikita, et al.
Published: (2024)
by: Balagansky, Nikita, et al.
Published: (2024)
ESSA: Evolutionary Strategies for Scalable Alignment
by: Korotyshova, Daria, et al.
Published: (2025)
by: Korotyshova, Daria, et al.
Published: (2025)
Next Embedding Prediction Makes World Models Stronger
by: Bredis, George, et al.
Published: (2026)
by: Bredis, George, et al.
Published: (2026)
Mashup Learning: Faster Finetuning by Remixing Past Checkpoints
by: Vaina, Sofia Maria Lo Cicero, et al.
Published: (2026)
by: Vaina, Sofia Maria Lo Cicero, et al.
Published: (2026)
Guided Star-Shaped Masked Diffusion
by: Meshchaninov, Viacheslav, et al.
Published: (2025)
by: Meshchaninov, Viacheslav, et al.
Published: (2025)
Reinforcement learning for question answering in programming domain using public community scoring as a human feedback
by: Gorbatovski, Alexey, et al.
Published: (2024)
by: Gorbatovski, Alexey, et al.
Published: (2024)
Learnable Permutation for Structured Sparsity on Transformer Models
by: Li, Zekai, et al.
Published: (2026)
by: Li, Zekai, et al.
Published: (2026)
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?
by: Soni, Nikita, et al.
Published: (2024)
by: Soni, Nikita, et al.
Published: (2024)
Transformer Based Linear Attention with Optimized GPU Kernel Implementation
by: Gerami, Armin, et al.
Published: (2025)
by: Gerami, Armin, et al.
Published: (2025)
LATMiX: Learnable Affine Transformations for Microscaling Quantization of LLMs
by: Gordon, Ofir, et al.
Published: (2026)
by: Gordon, Ofir, et al.
Published: (2026)
Alt-Text with Context: Improving Accessibility for Images on Twitter
by: Srivatsan, Nikita, et al.
Published: (2023)
by: Srivatsan, Nikita, et al.
Published: (2023)
VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks
by: Diatlova, Daria, et al.
Published: (2025)
by: Diatlova, Daria, et al.
Published: (2025)
On the Robustness of Transformers against Context Hijacking for Linear Classification
by: Li, Tianle, et al.
Published: (2025)
by: Li, Tianle, et al.
Published: (2025)
Alleviating Forgetfulness of Linear Attention by Hybrid Sparse Attention and Contextualized Learnable Token Eviction
by: He, Mutian, et al.
Published: (2025)
by: He, Mutian, et al.
Published: (2025)
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
by: Liu, Shang, et al.
Published: (2024)
by: Liu, Shang, et al.
Published: (2024)
Learnable Multi-Scale Wavelet Transformer: A Novel Alternative to Self-Attention
by: Kiruluta, Andrew, et al.
Published: (2025)
by: Kiruluta, Andrew, et al.
Published: (2025)
On the Learnability of Watermarks for Language Models
by: Gu, Chenchen, et al.
Published: (2023)
by: Gu, Chenchen, et al.
Published: (2023)
When an LLM is apprehensive about its answers -- and when its uncertainty is justified
by: Sychev, Petr, et al.
Published: (2025)
by: Sychev, Petr, et al.
Published: (2025)
Complexity-aware fine-tuning
by: Goncharov, Andrey, et al.
Published: (2025)
by: Goncharov, Andrey, et al.
Published: (2025)
Core Context Aware Transformers for Long Context Language Modeling
by: Chen, Yaofo, et al.
Published: (2024)
by: Chen, Yaofo, et al.
Published: (2024)
DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning
by: Ignatev, Daniil, et al.
Published: (2025)
by: Ignatev, Daniil, et al.
Published: (2025)
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
by: Sundaram, Shobhita, et al.
Published: (2026)
by: Sundaram, Shobhita, et al.
Published: (2026)
Quantization of Large Language Models with an Overdetermined Basis
by: Merkulov, Daniil, et al.
Published: (2024)
by: Merkulov, Daniil, et al.
Published: (2024)
Adaptive Two Sided Laplace Transforms: A Learnable, Interpretable, and Scalable Replacement for Self-Attention
by: Kiruluta, Andrew
Published: (2025)
by: Kiruluta, Andrew
Published: (2025)
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
by: Fu, Deqing, et al.
Published: (2023)
by: Fu, Deqing, et al.
Published: (2023)
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
by: Zhang, Ruiqi, et al.
Published: (2024)
by: Zhang, Ruiqi, et al.
Published: (2024)
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
by: Collins, Liam, et al.
Published: (2024)
by: Collins, Liam, et al.
Published: (2024)
Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models
by: Browder, Michael, et al.
Published: (2026)
by: Browder, Michael, et al.
Published: (2026)
Similar Items
-
Diffusion Language Models Generation Can Be Halted Early
by: Vaina, Sofia Maria Lo Cicero, et al.
Published: (2023) -
Learn Your Reference Model for Real Good Alignment
by: Gorbatovski, Alexey, et al.
Published: (2024) -
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
by: Laptev, Daniil, et al.
Published: (2025) -
You Do Not Fully Utilize Transformer's Representation Capacity
by: Gerasimov, Gleb, et al.
Published: (2025) -
Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders
by: Kurochkin, Vadim, et al.
Published: (2025)