| 1 |
Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation |
提出ViGOS框架以解决多模态自蒸馏中的快捷问题 |
distillation large language model multimodal |
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| 2 |
Dual-Channel Grounded World Modeling (DCGWM): Structural Prevention of Objective Interference Collapse via Heterogeneous External Grounding with Inward-Only Gradient Flow |
提出双通道基础世界建模以解决目标干扰崩溃问题 |
world model world models JEPA |
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| 3 |
Reinforcement Learning Foundation Models Should Already Be A Thing |
提出强化学习基础模型以填补现有方法空白 |
reinforcement learning foundation model |
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| 4 |
The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL |
提出判别器引导的强化学习以解决流匹配问题 |
reinforcement learning DRL flow matching |
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| 5 |
Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation |
提出TAPO以解决自蒸馏中的错误诊断问题 |
distillation large language model |
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| 6 |
Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards |
提出SC-GRPO以解决强化学习中的信用分配问题 |
reinforcement learning distillation privileged information |
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| 7 |
UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning |
提出UBP2以解决偏好强化学习中的样本效率问题 |
reinforcement learning reward design |
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| 8 |
Wasserstein Policy Learning for Distributional Outcomes |
提出Wasserstein策略学习以解决离线政策学习中的分布值结果问题 |
policy learning |
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| 9 |
REVES: REvision and VErification--Augmented Training for Test-Time Scaling |
提出REVES以解决大语言模型推理中的多步骤优化问题 |
reinforcement learning large language model |
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| 10 |
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution |
提出生命周期感知动态分析以保障机器学习模型执行安全 |
world model world models |
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| 11 |
Maturing Markov Decision Processes: Decision Making under Increasing Information and Shrinking Action Sets |
提出成熟马尔可夫决策过程以解决信息不对称问题 |
reinforcement learning distillation |
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