On the Reasoning Abilities of Masked Diffusion Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Svete, Anej, Sabharwal, Ashish |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
by: Svete, Anej, et al.
Published: (2026)
by: Svete, Anej, et al.
Published: (2026)
Transformers Can Represent $n$-gram Language Models
by: Svete, Anej, et al.
Published: (2024)
by: Svete, Anej, et al.
Published: (2024)
Gumbel Counterfactual Generation From Language Models
by: Ravfogel, Shauli, et al.
Published: (2024)
by: Ravfogel, Shauli, et al.
Published: (2024)
On the Representational Capacity of Recurrent Neural Language Models
by: Nowak, Franz, et al.
Published: (2023)
by: Nowak, Franz, et al.
Published: (2023)
Towards Reasoning Ability of Small Language Models
by: Srivastava, Gaurav, et al.
Published: (2025)
by: Srivastava, Gaurav, et al.
Published: (2025)
ADaPT: As-Needed Decomposition and Planning with Language Models
by: Prasad, Archiki, et al.
Published: (2023)
by: Prasad, Archiki, et al.
Published: (2023)
Soft-Masked Diffusion Language Models
by: Hersche, Michael, et al.
Published: (2025)
by: Hersche, Michael, et al.
Published: (2025)
Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
by: Wang, Xinyi, et al.
Published: (2024)
by: Wang, Xinyi, et al.
Published: (2024)
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
by: Lin, Bill Yuchen, et al.
Published: (2025)
by: Lin, Bill Yuchen, et al.
Published: (2025)
Simple and Effective Masked Diffusion Language Models
by: Sahoo, Subham Sekhar, et al.
Published: (2024)
by: Sahoo, Subham Sekhar, et al.
Published: (2024)
On Efficiently Representing Regular Languages as RNNs
by: Svete, Anej, et al.
Published: (2024)
by: Svete, Anej, et al.
Published: (2024)
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models
by: Chen, Changyu, et al.
Published: (2024)
by: Chen, Changyu, et al.
Published: (2024)
Data-driven Discovery with Large Generative Models
by: Majumder, Bodhisattwa Prasad, et al.
Published: (2024)
by: Majumder, Bodhisattwa Prasad, et al.
Published: (2024)
Understanding and Accelerating the Training of Masked Diffusion Language Models
by: Hong, Chunsan, et al.
Published: (2026)
by: Hong, Chunsan, et al.
Published: (2026)
QualEval: Qualitative Evaluation for Model Improvement
by: Murahari, Vishvak, et al.
Published: (2023)
by: Murahari, Vishvak, et al.
Published: (2023)
Variational Masked Diffusion Models
by: Zhang, Yichi, et al.
Published: (2025)
by: Zhang, Yichi, et al.
Published: (2025)
$π^2$: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models
by: Do, Quyet V., et al.
Published: (2026)
by: Do, Quyet V., et al.
Published: (2026)
ILRR: Inference-Time Steering Method for Masked Diffusion Language Models
by: Avrahami, Eden, et al.
Published: (2026)
by: Avrahami, Eden, et al.
Published: (2026)
DiscoveryBench: Towards Data-Driven Discovery with Large Language Models
by: Majumder, Bodhisattwa Prasad, et al.
Published: (2024)
by: Majumder, Bodhisattwa Prasad, et al.
Published: (2024)
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
by: Ji, Kaixuan, et al.
Published: (2024)
by: Ji, Kaixuan, et al.
Published: (2024)
Masked Diffusion Models as Energy Minimization
by: Chen, Sitong, et al.
Published: (2025)
by: Chen, Sitong, et al.
Published: (2025)
Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
by: Ye, Jiacheng, et al.
Published: (2024)
by: Ye, Jiacheng, et al.
Published: (2024)
Unlocking Continual Learning Abilities in Language Models
by: Du, Wenyu, et al.
Published: (2024)
by: Du, Wenyu, et al.
Published: (2024)
Deception Abilities Emerged in Large Language Models
by: Hagendorff, Thilo
Published: (2023)
by: Hagendorff, Thilo
Published: (2023)
Scaling up Masked Diffusion Models on Text
by: Nie, Shen, et al.
Published: (2024)
by: Nie, Shen, et al.
Published: (2024)
Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models
by: Kong, Injin, et al.
Published: (2026)
by: Kong, Injin, et al.
Published: (2026)
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow
by: Zhong, Yangyang, et al.
Published: (2026)
by: Zhong, Yangyang, et al.
Published: (2026)
Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
by: Ding, Fangyu, et al.
Published: (2026)
by: Ding, Fangyu, et al.
Published: (2026)
Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling
by: Zheng, Kaiwen, et al.
Published: (2024)
by: Zheng, Kaiwen, et al.
Published: (2024)
Simple Policy Gradients for Reasoning with Diffusion Language Models
by: Zhan, Anthony
Published: (2025)
by: Zhan, Anthony
Published: (2025)
Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models
by: Petruzzellis, Flavio, et al.
Published: (2024)
by: Petruzzellis, Flavio, et al.
Published: (2024)
Self-Evolving Critique Abilities in Large Language Models
by: Tang, Zhengyang, et al.
Published: (2025)
by: Tang, Zhengyang, et al.
Published: (2025)
Emergent Abilities in Large Language Models: A Survey
by: Berti, Leonardo, et al.
Published: (2025)
by: Berti, Leonardo, et al.
Published: (2025)
Distilling LLMs' Decomposition Abilities into Compact Language Models
by: Tarasov, Denis, et al.
Published: (2024)
by: Tarasov, Denis, et al.
Published: (2024)
Adaptive Guidance for Retrieval-Augmented Masked Diffusion Models
by: Kim, Jaemin, et al.
Published: (2026)
by: Kim, Jaemin, et al.
Published: (2026)
NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes
by: Fan, Lizhou, et al.
Published: (2023)
by: Fan, Lizhou, et al.
Published: (2023)
Unique Hard Attention: A Tale of Two Sides
by: Jerad, Selim, et al.
Published: (2025)
by: Jerad, Selim, et al.
Published: (2025)
Understanding Emergent Abilities of Language Models from the Loss Perspective
by: Du, Zhengxiao, et al.
Published: (2024)
by: Du, Zhengxiao, et al.
Published: (2024)
Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty
by: Chen, Ziyu, et al.
Published: (2025)
by: Chen, Ziyu, et al.
Published: (2025)
Fisher Mask Nodes for Language Model Merging
by: K, Thennal D, et al.
Published: (2024)
by: K, Thennal D, et al.
Published: (2024)
Similar Items
-
Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
by: Svete, Anej, et al.
Published: (2026) -
Transformers Can Represent $n$-gram Language Models
by: Svete, Anej, et al.
Published: (2024) -
Gumbel Counterfactual Generation From Language Models
by: Ravfogel, Shauli, et al.
Published: (2024) -
On the Representational Capacity of Recurrent Neural Language Models
by: Nowak, Franz, et al.
Published: (2023) -
Towards Reasoning Ability of Small Language Models
by: Srivastava, Gaurav, et al.
Published: (2025)