Portrait de Reza Bayat

Reza Bayat

Doctorat - UdeM
Superviseur⋅e principal⋅e
Aaron Courville
Co-supervisor
Pascal Vincent
Sujets de recherche
Apprentissage profond
Modèles génératifs
Site web

Publications

Steering Large Language Model Activations in Sparse Spaces
Reza Bayat
Ali Rahimi-Kalahroudi
Mohammad Pezeshki
Sarath Chandar
Pascal Vincent
2025-07-07
colmweb.org/COLM/2025/Conference (accepté)
doi.org
openreview.net
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Thinking
Sangmin Bae
Yujin Kim
Reza Bayat
Sungnyun Kim
Jiyoun Ha
Tal Schuster
Adam Fisch
Hrayr Harutyunyan
Ziwei Ji
Aaron Courville
Se-Young Yun
Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deploy… (voir plus)ment expensive. Existing efficiency efforts typically target either parameter sharing or adaptive computation, leaving open the question of how to attain both simultaneously. We introduce Mixture-of-Recursions (MoR), a unified framework that combines the two axes of efficiency inside a single Recursive Transformer. MoR reuses a shared stack of layers across recursion steps to achieve parameter efficiency, while lightweight routers enable adaptive token-level thinking by dynamically assign recursion depth to tokens, thereby focusing quadratic attention computation only where it is most useful. Further enhancing its efficiency, MoR incorporates a recursion-wise key-value caching mechanism that eliminates redundant memory access across recursion steps by selectively storing only the key-value caches for designated tokens. Across pretraining runs at model scales ranging from 135M to 1.7B parameters, MoR forms a new Pareto frontier: at equal training FLOPs and smaller model sizes, it significantly lowers validation perplexity and improves few-shot accuracy, while delivering higher throughput compared with vanilla and existing recursive baselines. These gains demonstrate that MoR is an effective path towards large-model quality without incurring large-model cost.
2025-06-11
ICML.cc/2025/Workshop/ES-FoMo-III (publié)
openreview.net
openreview.net
Steering Large Language Model Activations in Sparse Spaces
Reza Bayat
Ali Rahimi-Kalahroudi
Mohammad Pezeshki
Sarath Chandar
Pascal Vincent
A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time. Activation steering, which… (voir plus) modifies internal model activations during inference, offers a potential solution. However, prior work in dense activation spaces struggles with superposition, wherein multiple features become entangled, limiting interpretability and precise control. In contrast, sparse representations provide an untapped opportunity for more interpretable behavior modulation. In this work, we introduce sparse activation steering (SAS), a method that leverages sparse autoencoders (SAEs) to steer LLM behavior in sparse spaces. By isolating behavior-specific features through a contrastive prompt-pairing approach, we define a set of features that can selectively reinforce or suppress behaviors. Experiments on Gemma 2 LLMs show that SAS vectors enable nuanced behavioral modulation and finer-grained control. Furthermore, scaling SAEs improves monosemanticity of SAS vectors, suggesting more reliable and interpretable interventions.
2025-02-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Steering Large Language Model Activations in Sparse Spaces
Reza Bayat
Ali Rahimi-Kalahroudi
Mohammad Pezeshki
Sarath Chandar
Pascal Vincent
A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time. Activation steering, which… (voir plus) modifies internal model activations during inference, offers a potential solution. However, prior work in dense activation spaces struggles with superposition, wherein multiple features become entangled, limiting interpretability and precise control. In contrast, sparse representations provide an untapped opportunity for more interpretable behavior modulation. In this work, we introduce sparse activation steering (SAS), a method that leverages sparse autoencoders (SAEs) to steer LLM behavior in sparse spaces. By isolating behavior-specific features through a contrastive prompt-pairing approach, we define a set of features that can selectively reinforce or suppress behaviors. Experiments on Gemma 2 LLMs show that SAS vectors enable nuanced behavioral modulation and finer-grained control. Furthermore, scaling SAEs improves monosemanticity of SAS vectors, suggesting more reliable and interpretable interventions.
2025-02-28
ArXiv (prépublication)
doi.org
arxiv.org
The Pitfalls of Memorization: When Memorization Hurts Generalization
Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (voir plus)ations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious correlations would particularly lead to poor generalization when are combined with memorization. Memorization can reduce training loss to zero, leaving no incentive to learn robust, generalizable patterns. To address this, we propose memorization-aware training (MAT), which uses held-out predictions as a signal of memorization to shift a model's logits. MAT encourages learning robust patterns invariant across distributions, improving generalization under distribution shifts.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Learning adversarially robust kernel ensembles with kernel average pooling
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2024-12-01
Expert systems with applications (publié)
doi.org
Learning adversarially robust kernel ensembles with kernel average pooling
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2024-12-01
Expert systems with applications (publié)
doi.org
Learning adversarially robust kernel ensembles with kernel average pooling
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2024-12-01
Expert systems with applications (publié)
doi.org
The Pitfalls of Memorization: When Memorization Hinders Generalization
Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (voir plus)ations. This leads to poor generalization when the learned explanations are spurious. In this work, we formalize
2024-10-10
NeurIPS.cc/2024/Workshop/SciForDL (poster)
openreview.net
openreview.net
The Pitfalls of Memorization: When Memorization Hinders Generalization
Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (voir plus)ations. This leads to poor generalization when the learned explanations are spurious. In this work, we formalize
2024-10-10
NeurIPS.cc/2024/Workshop/SciForDL (poster)
openreview.net
openreview.net
Adversarial Training with Synthesized Data: A Path to Robust and Generalizable Neural Networks
Reza Bayat
Irina Rish
Adversarial Training (AT) is a well-known framework designed to mitigate adversarial vulnerabilities in neural networks. Recent research ind… (voir plus)icates that incorporating adversarial examples (AEs) in training can enhance models' generalization capabilities. To understand the impact of AEs on learning dynamics, we study AT through the lens of sample difficulty methodologies. Our findings show that AT leads to more stable learning dynamics compared to Natural Training (NT), resulting in gradual performance improvements and less overconfident predictions. This suggests that AT steers training away from learning easy, perturbable spurious features toward more resilient and generalizable ones. However, a trade-off exists between adversarial robustness and generalization gains, due to robust overfitting, limiting practical deployment. To address this, we propose using synthesized data to bridge this gap. Our results demonstrate that AT benefits significantly from synthesized data, whereas NT does not, enhancing generalization without compromising robustness and offering new avenues for developing robust and generalizable models.
2024-06-28
ICML.cc/2024/Workshop/NextGenAISafety (poster)
openreview.net
openreview.net
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
Rishika Bhagwatkar
Shravan Nayak
Reza Bayat
Alexis Roger
Daniel Z Kaplan
Pouya Bashivan
Irina Rish
Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. However, as they becoming increasingly pr… (voir plus)evalent, ensuring their robustness against adversarial attacks is paramount. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks. Additionally, we introduce novel, cost-effective approaches to enhance robustness through prompt formatting. By rephrasing questions and suggesting potential adversarial perturbations, we demonstrate substantial improvements in model robustness against strong image-based attacks such as Auto-PGD. Our findings provide important guidelines for developing more robust VLMs, particularly for deployment in safety-critical environments.
2024-06-28
ICML.cc/2024/Workshop/NextGenAISafety (poster)
doi.org
openreview.net