The Fourteenth International Conference on Learning Representations (ICLR 2026)

ICLR, 2026.

The Fourteenth International Conference on Learning Representations

Chapters

GraphPFN: A Prior-Data Fitted Graph Foundation Model

Eremeev D., Babenko A., Platonov O. et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. P. 1–16.

Graph foundation models face several fundamental challenges including transferability across datasets and data scarcity, which calls into question the feasibility of graph foundation models at all. However, despite similar challenges, the tabular domain has recently witnessed the emergence of the first successful foundation models such as TabPFNv2 or LimiX. Many of these models are based ...

Added: February 16, 2026

Turning Tabular Foundation Models into Graph Foundation Models

Eremeev D., Bazhenov G., Platonov O. et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. P. 1–17.

While foundation models have revolutionized such fields as natural language processing and computer vision, their potential in graph machine learning remains largely unexplored. One of the key challenges in designing graph foundation models (GFMs) is handling diverse node features that can vary across different graph datasets. While many works on GFMs have focused exclusively on ...

Added: February 16, 2026

Rethinking Global Text Conditioning in Diffusion Transformers

Nikita Starodubcev, Pakhomov D., Wu Z. et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026.

Diffusion transformers typically incorporate textual information via (i) attention layers and (ii) a modulation mechanism using a pooled text embedding. Nevertheless, recent approaches discard modulation-based text conditioning and rely exclusively on attention. In this paper, we address whether modulation-based text conditioning is necessary and whether it can provide any performance advantage. Our analysis shows that, ...

Added: February 17, 2026

Scale-wise Distillation of Diffusion Models

Nikita Starodubcev, Ilya Drobyshevskiy, Denis Kuznedelev et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026.

Recent diffusion distillation methods have achieved remarkable progress, enabling high-quality -step sampling for large-scale text-conditional image and video diffusion models (DMs). However, further reducing the number of sampling steps becomes more and more challenging, suggesting that efficiency gains may be better mined along other model axes. Motivated by this perspective, we introduce SwD, a scale-wise diffusion ...

Added: February 17, 2026

LoRA meets Riemannion: Muon Optimizer for Parametrization-independent Low-Rank Adapters

Vladimir Bogachev, Aletov V., Alexander Molozhavenko et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. Ch. 20503 P. 1–26.

This work presents a novel, fully Riemannian framework for Low-Rank Adaptation (LoRA) that geometrically treats low-rank adapters by optimizing them directly on the fixed-rank manifold. This formulation eliminates the parametrization ambiguity present in standard Euclidean optimizers. Our framework integrates three key components to achieve this: (1) we derive Riemannion, a new Riemannian optimizer on the fixed-rank ...

Added: April 29, 2026

GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver

Oganov A., Bykov I., Neudachina E. et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. Ch. 18372.

While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based optimization methods that distill a few-step ODE diffusion solver from the full sampling process, reducing the number of function evaluations from dozens to just a few. However, these approaches often rely on intricate training ...

Added: July 13, 2026

Keywords: machine learning