Lookahead Path Likelihood Optimization for Diffusion LLMs
Fuente:
arXiv
Guardado en:
| Autores principales: | Liu, Xuejie, Chun, Yap Vit, Liang, Yitao, Liu, Anji |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Tractable Inference Perspective of Offline RL
por: Liu, Xuejie, et al.
Publicado: (2023)
por: Liu, Xuejie, et al.
Publicado: (2023)
Tractable Transformers for Flexible Conditional Generation
por: Liu, Anji, et al.
Publicado: (2025)
por: Liu, Anji, et al.
Publicado: (2025)
The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
por: Zhao, Zhiyu, et al.
Publicado: (2026)
por: Zhao, Zhiyu, et al.
Publicado: (2026)
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
por: He, Kaichen, et al.
Publicado: (2025)
por: He, Kaichen, et al.
Publicado: (2025)
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
por: Cai, Shaofei, et al.
Publicado: (2025)
por: Cai, Shaofei, et al.
Publicado: (2025)
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
por: Wang, Zihao, et al.
Publicado: (2024)
por: Wang, Zihao, et al.
Publicado: (2024)
Generalization and Optimization of SGD with Lookahead
por: Li, Kangcheng, et al.
Publicado: (2025)
por: Li, Kangcheng, et al.
Publicado: (2025)
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents
por: Cai, Shaofei, et al.
Publicado: (2024)
por: Cai, Shaofei, et al.
Publicado: (2024)
Discrete Copula Diffusion
por: Liu, Anji, et al.
Publicado: (2024)
por: Liu, Anji, et al.
Publicado: (2024)
Plug-and-Play Context Feature Reuse for Efficient Masked Generation
por: Liu, Xuejie, et al.
Publicado: (2025)
por: Liu, Xuejie, et al.
Publicado: (2025)
Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks
por: Trainin, Itamar, et al.
Publicado: (2026)
por: Trainin, Itamar, et al.
Publicado: (2026)
Image Inpainting via Tractable Steering of Diffusion Models
por: Liu, Anji, et al.
Publicado: (2023)
por: Liu, Anji, et al.
Publicado: (2023)
Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
por: Lee, Sanghyun, et al.
Publicado: (2025)
por: Lee, Sanghyun, et al.
Publicado: (2025)
Next-Depth Lookahead Tree
por: Lee, Jaeho, et al.
Publicado: (2025)
por: Lee, Jaeho, et al.
Publicado: (2025)
Reinforcement Learning with Lookahead Information
por: Merlis, Nadav
Publicado: (2024)
por: Merlis, Nadav
Publicado: (2024)
Lookahead Counterfactual Fairness
por: Zuo, Zhiqun, et al.
Publicado: (2024)
por: Zuo, Zhiqun, et al.
Publicado: (2024)
Learning to Discretize Denoising Diffusion ODEs
por: Tong, Vinh, et al.
Publicado: (2024)
por: Tong, Vinh, et al.
Publicado: (2024)
OSS-Bench: Benchmark Generator for Coding LLMs
por: Jiang, Yuancheng, et al.
Publicado: (2025)
por: Jiang, Yuancheng, et al.
Publicado: (2025)
Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
por: Kim, Yeongmin, et al.
Publicado: (2026)
por: Kim, Yeongmin, et al.
Publicado: (2026)
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detection
por: Huang, Sicong, et al.
Publicado: (2024)
por: Huang, Sicong, et al.
Publicado: (2024)
The Value of Reward Lookahead in Reinforcement Learning
por: Merlis, Nadav, et al.
Publicado: (2024)
por: Merlis, Nadav, et al.
Publicado: (2024)
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion
por: Tong, Vinh, et al.
Publicado: (2025)
por: Tong, Vinh, et al.
Publicado: (2025)
Causal Attention with Lookahead Keys
por: Song, Zhuoqing, et al.
Publicado: (2025)
por: Song, Zhuoqing, et al.
Publicado: (2025)
Policy Mirror Descent with Lookahead
por: Protopapas, Kimon, et al.
Publicado: (2024)
por: Protopapas, Kimon, et al.
Publicado: (2024)
EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
por: Yau, Chung-Yiu, et al.
Publicado: (2026)
por: Yau, Chung-Yiu, et al.
Publicado: (2026)
Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming
por: Zheng, Liang, et al.
Publicado: (2026)
por: Zheng, Liang, et al.
Publicado: (2026)
LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models
por: Wei, Chenxing, et al.
Publicado: (2026)
por: Wei, Chenxing, et al.
Publicado: (2026)
Scaling Tractable Probabilistic Circuits: A Systems Perspective
por: Liu, Anji, et al.
Publicado: (2024)
por: Liu, Anji, et al.
Publicado: (2024)
Zero-Variance Gradients for Variational Autoencoders
por: Shao, Zilei, et al.
Publicado: (2025)
por: Shao, Zilei, et al.
Publicado: (2025)
Rethinking Probabilistic Circuit Parameter Learning
por: Liu, Anji, et al.
Publicado: (2025)
por: Liu, Anji, et al.
Publicado: (2025)
LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
por: Paulus, Anselm, et al.
Publicado: (2024)
por: Paulus, Anselm, et al.
Publicado: (2024)
Scaling Speculative Decoding with Lookahead Reasoning
por: Fu, Yichao, et al.
Publicado: (2025)
por: Fu, Yichao, et al.
Publicado: (2025)
Lookahead identification in adversarial bandits: accuracy and memory bounds
por: Brukhim, Nataly, et al.
Publicado: (2026)
por: Brukhim, Nataly, et al.
Publicado: (2026)
Breaking the Factorization Barrier in Diffusion Language Models
por: Li, Ian, et al.
Publicado: (2026)
por: Li, Ian, et al.
Publicado: (2026)
Lookahead Drifting Model
por: Zhang, Guoqiang, et al.
Publicado: (2026)
por: Zhang, Guoqiang, et al.
Publicado: (2026)
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
por: Cheon, Mujin, et al.
Publicado: (2024)
por: Cheon, Mujin, et al.
Publicado: (2024)
RulE: Knowledge Graph Reasoning with Rule Embedding
por: Tang, Xiaojuan, et al.
Publicado: (2022)
por: Tang, Xiaojuan, et al.
Publicado: (2022)
DDPS: Discrete Diffusion Posterior Sampling for Paths in Layered Graphs
por: Luan, Hao, et al.
Publicado: (2025)
por: Luan, Hao, et al.
Publicado: (2025)
Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction
por: Blondel, Mathieu, et al.
Publicado: (2025)
por: Blondel, Mathieu, et al.
Publicado: (2025)
Thinking into the Future: Latent Lookahead Training for Transformers
por: Noci, Lorenzo, et al.
Publicado: (2026)
por: Noci, Lorenzo, et al.
Publicado: (2026)
Ejemplares similares
-
A Tractable Inference Perspective of Offline RL
por: Liu, Xuejie, et al.
Publicado: (2023) -
Tractable Transformers for Flexible Conditional Generation
por: Liu, Anji, et al.
Publicado: (2025) -
The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
por: Zhao, Zhiyu, et al.
Publicado: (2026) -
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
por: He, Kaichen, et al.
Publicado: (2025) -
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
por: Cai, Shaofei, et al.
Publicado: (2025)