Announcing the ICML 2026 Awards
By ICML 2026 Program Chairs Alekh Agarwal, Miroslav Dudik, Sharon Li, Martin Jaggi and ICML 2026 Position Paper Track Chairs Dale Schuurmans and Jerry Zhu.
We are pleased to announce the following award recipients, to be recognized at ICML 2026! More details on the selected papers and the selection process follow.
ICML 2026 Outstanding Paper Award:
The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models
Zanlin Ni, Shenzhi Wang, Yang Yue, Tianyu Yu, Weilin Zhao, Yeguo Hua, Tianyi Chen, Jun Song, Cheng Yu, Bo Zheng, Gao Huang
High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions
Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin
ICML 2026 Outstanding Position Paper Award:
Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit
Sarah Ball, Phil Hackemann
ICML 2026 Outstanding Paper Honorable Mention:
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
Mohammad Taufeeque, Stefan Heimersheim, Adam Gleave, Chris Cundy
Motion Attribution for Video Generation
Xindi Wu, Despoina Paschalidou, Jun Gao, Antonio Torralba, Laura Leal-Taixé, Olga Russakovsky, Sanja Fidler, Jonathan Lorraine
How much can language models memorize?
John Xavier Morris, Chawin Sitawarin, Narine Kokhlikyan, Chuan Guo, G. Edward Suh, Alexander M Rush, Kamalika Chaudhuri, Saeed Mahloujifar
A Random Matrix Perspective on the Consistency of Diffusion Models
Binxu Wang, Jacob A Zavatone-Veth, Cengiz Pehlevan
To Grok Grokking: Provable Grokking in Ridge Regression
Mingyue Xu, Gal Vardi, Itay Safran
ICML 2026 Outstanding Position Paper Honorable Mention:
Position: AI/ML Deepfake Research is Misaligned with AI Generated Non-Consensual Intimate Imagery (AIG-NCII)
Li Qiwei, Wells Lucas Santo, Sarita Schoenebeck, Eric Gilbert
ICML 2026 Test of Time Award:
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
ICML 2026 Outstanding Paper Award
The ICML 2026 program chairs (Alekh Agarwal, Miro Dudik, Martin Jaggi, and Sharon Li) selected 53 initial award candidates, based on a combination of signals from Reviewer scores and Area Chair nominations. Papers were explicitly included from across the eight top-level subject areas, to compensate for differences in standards across areas and ensure that each of the topic areas gets some representation in consideration.
The initial set of 53 papers was reviewed by program chairs, who selected a short list of 22 papers. The key criteria were: strong-accept quality, perceived nontrivial “longevity potential,” interest beyond a niche subcommunity, and topic diversity.
The short list was then passed to the Outstanding Paper selection committee. This committee had 11 members, including Andreas Krause (chair), Yoav Artzi, Leon Bottou, Michael Bowling, Jordan Lee Boyd-Graber, Marco Cuturi, Aleksandra Faust, Claudio Gentile, Amir Globerson, Manik Varma, and Yu-Xiang Wang.
Committee members received anonymized versions of the papers. Each paper was reviewed by two committee members (one of them marked as primary and one of them marked as secondary), and each committee member reviewed 3-5 papers (to help with calibration). Committee members were explicitly told that there is no set quota on the number of awards (so, in principle, all 22 shortlisted papers could be selected). A small set of conflicts of interests was marked (two of the shortlisted candidates had conflicts with two committee members each; no member was conflicted with more than one paper); conflicted committee members were asked to recuse themselves from decision making on the paper where they had a conflict.
The committee met and discussed every paper. For each paper, the primary reviewer provided a short summary and assessment, followed by the secondary reviewer. For the papers where at least one reviewer suggested a recognition, the committee discussed it in more detail with the entire committee. This way, the committee identified candidates for outstanding papers and honorable mentions. For the cases where the committee felt they needed more information, they reached out to additional experts offline to make the final decision. In the end, the committee chose to recognize two Outstanding Papers and five Honorable Mentions.
Outstanding Papers
The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models
Zanlin Ni, Shenzhi Wang, Yang Yue, Tianyu Yu, Weilin Zhao, Yeguo Hua, Tianyi Chen, Jun Song, Cheng Yu, Bo Zheng, Gao Huang
With the context that diffusion large language models (dLLMs) are progressively cementing their status as viable contenders to the autoregressive paradigm for language generation, this paper offers a refreshingly counter-intuitive view that challenges one of the field’s dominant assumptions. While arbitrary-order (e.g., confidence-based) generation is often presented as the main feature of dLLMs that allows for a faster generation, the authors convincingly show that on general reasoning tasks dLLMs exploit this freedom to bypass exactly the high-uncertainty “forking” tokens that matter most, collapsing solution diversity. This is a non-obvious failure mode that was far from evident before this work. Based on this insight, the authors propose to revisit post-training for dLLMs using a simpler fixed left-to-right generation order for reinforcement learning (RL) rollouts (their JustGRPO recipe), while retaining parallel decoding at inference. That new approach highlights the still under-explored question of which sampling strategy should drive RL rollouts in dLLMs.
High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions
Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin
This paper settles a long-standing question in the theory of score-based sampling: whether ε-error can be achieved in polylog(1/ε) steps using only score (gradient) evaluations, rather than the poly(1/ε) steps that discretization-based samplers require. The key construction is first-order rejection sampling (FORS), a meta-algorithm that simulates rejection sampling from first-order queries alone, bypassing density evaluations that previous high-accuracy methods relied on. Under minimal assumptions on the data, this yields an O(d·polylog(1/ε)) sampler, an exponential improvement over prior score-based results, with refinements under non-uniform Lipschitz and intrinsic-dimension conditions, and as a byproduct, the first polylog(1/ε) gradient-only sampler for general log-concave distributions. For diffusion models specifically, the result shows that the number of denoising steps (score-function evaluations) needed to reach a target sample accuracy can in principle drop from polynomial to polylogarithmic in 1/ε, suggesting that highly accurate diffusion sampling can be obtained with a smaller number of function evaluations (NFEs).
Outstanding Paper Honorable Mentions
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
Mohammad Taufeeque, Stefan Heimersheim, Adam Gleave, Chris Cundy
This paper provides a rigorous investigation into the risks of training large language models against white-box linear lie detectors within a realistic code optimization landscape that is prone to reward hacking. A major contribution of this work is its combination of empirical findings in a coding environment with theoretical proofs, successfully demonstrating that policy gradient methods do not generate direct optimization pressure toward activation manipulation. To understand how models bypass these detectors, the authors establish a helpful taxonomy of training outcomes—identifying blatant deception, obfuscated activations, and obfuscated policies. Crucially, the research provides useful engineering foundations for scalable oversight by showing that alignment failures can be mitigated through high KL regularization paired with strong detector penalties. While the framing of model deception and honesty can appear somewhat anthropomorphic, the paper ultimately tackles an important problem and provides highly useful insights for the field of AI alignment.
Motion Attribution for Video Generation
Xindi Wu, Despoina Paschalidou, Jun Gao, Antonio Torralba, Laura Leal-Taixé, Olga Russakovsky, Sanja Fidler, Jonathan Lorraine
Every machine learning practitioner knows that carefully choosing the training data can significantly improve model performance. To enhance motion quality in video generation, this paper introduces an attribution method that tracks the contribution of individual training examples to the relevant metrics on test cases. A clever numerical trick makes it work at scale. Thanks to this attribution information, superior performance is achieved using as little as one-tenth of the original training set.
How much can language models memorize?
John Xavier Morris, Chawin Sitawarin, Narine Kokhlikyan, Chuan Guo, G. Edward Suh, Alexander M Rush, Kamalika Chaudhuri, Saeed Mahloujifar
There’s a big question about how much LLMs are regurgitating stored knowledge and how much they’re generalizing. This paper proposes that one could measure Kolmogorov memorization: how much of a distribution is explained by what is learned by a model. But the paper further breaks this down into intended memorization (generalization) and unintended memorization. The theoretical approach of this paper to distinguish unintended from intended memorization is a novel perspective to think about what LLMs are achieving, and this strong theoretical foundation is complemented by an empirical measurement of what happens in practice. Through this lens, the paper makes the bold claim that GPT-style language models can model 3.6 bits of a distribution per model parameter.
A Random Matrix Perspective on the Consistency of Diffusion Models
Binxu Wang, Jacob A Zavatone-Veth, Cengiz Pehlevan
Recent empirical studies have shown that diffusion models exhibit a high level of across-run
reproducibility under deterministic sampling. Even when trained on separate data splits, different architectures will map a single noise seed to nearly identical image outputs. This paper demystifies this phenomenon by proving that diffusion consistency is fundamentally rooted in shared linear Gaussian statistics. In particular, by relying on a linear denoiser setup to track how finite-sample dataset fluctuations distort generated outputs, the authors show that this consistency is not due to complex, high-order deep learning dynamics or data memorization, but it has a linear origin: the shared empirical Gaussian statistics (mean and covariance) across data splits are already enough to predict much of the cross-split consistency. The paper is a tour de force that turns an uncanny empirical quirk into an elegant, mathematically principled baseline for generative reproducibility. By beautifully linking the spectral geometry of data to the stability of model outputs, the authors have provided a foundational cornerstone that will shape the future study of generalization in diffusion models.
To Grok Grokking: Provable Grokking in Ridge Regression
Mingyue Xu, Gal Vardi, Itay Safran
Grokking is the phenomenon in training models where they quickly overfit but generalize badly, and then over time get to a solution that generalizes well. This phenomenon is interesting because it suggests when the model learns the “true” underlying structure of complex problems. Recent works (e.g., Lyu et al., ICLR 2024) have provided an analysis of this phenomenon for deep models, but do not provide global convergence results. The current paper studies grokking for a purely linear model, demonstrating that a two phase behavior emerges in ridge regression. This is a useful finding because it shows that grokking can emerge in this simple setting, and this can be used as a “toy” model for grokking, similar to how deep linear networks have proven useful for analyzing non-linear networks.
ICML 2026 Outstanding Position Paper Award
Selection of the ICML 2026 Outstanding Position Papers was headed by the Track co-chairs, Dale Schuurmans and Jerry Zhu. Nominations were solicited from the Area Chairs for the Position Paper Track. The Track co-chairs then each independently read and assessed all nominated papers. After their independent assessments, the ranking of nominated papers was found to be nearly identical, with two papers unanimously considered to be the most worthy of distinction. In their view, these two papers exemplify the ideals of the Position Track, from framing important but neglected issues, providing clear evidential support for the main claims, carefully considering alternative positions, and developing practical calls to action that should influence work in their respective areas. After discussion and consideration of all the factors outlined in the Call for Position Papers, one Outstanding Position Paper and one Honorable Mention were identified.
Outstanding Position Paper
Position: The Alignment Community is Unintentionally Building a Censor’s Toolkit
Sarah Ball, Phil Hackemann
This paper challenges the comfortable assumption held by some of us that we are a force for good, asserting that even value alignment—the primary tool we build to ensure AI does no harm—can be misused. The paper supports this assertion with compelling, real-world evidence. Notably, the paper is not about any individual country or company; its scope generalizes to all authorities, present and future. Finally, it proposes several directions to mitigate alignment misuse, raising awareness of the dual-use nature of the technologies our community develops.
Outstanding Position Paper Honorable Mention
Position: AI/ML Deepfake Research is Misaligned with AI Generated Non-Consensual Intimate Imagery (AIG-NCII)
Li Qiwei, Wells Lucas Santo, Sarita Schoenebeck, Eric Gilbert
This paper addresses an important yet uncomfortable issue: identifying the misalignment between current deepfake research and the proliferation of AI-generated non-consensual intimate imagery (AIG-NCII). The paper clearly illustrates the gap between the current technical focus in the literature and the pervasive harm of AIG-NCII, demonstrating how the current neglect of victim impact is exacerbating rather than mitigating the negative impacts of AIG-NCII. A cogent list of research imperatives is proposed to address the significant harm of AIG-NCII. This paper needs to be read by everyone who conducts research in this space.
ICML 2026 Test of Time Award
The selection process for the ICML 2026 Test of Time award was orchestrated by Kilian Weinberger, program co-chair of ICML 2016. All papers accepted to ICML 2016 were considered. By identifying influential papers based on citations and scholarly reputation, a shortlist of eight candidates was constructed. Leading researchers in the respective sub-fields were consulted, to assess the long-term impact and suitability of each candidate publication. One paper emerged as a clear winner.
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
This paper pioneered asynchronous reinforcement learning (RL), which has been a major contributing factor to the success of RL in LLM post-training and has reshaped the way RL is performed in practice. The insight that parallel actor-learners stabilize learning has since inspired numerous successors and has built a lasting legacy within the machine learning community.