| 1 |
Teaching Large Language Models to Reason with Reinforcement Learning |
通过强化学习提升大型语言模型的推理能力 |
reinforcement learning PPO RLHF |
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| 2 |
Mastering Memory Tasks with World Models |
提出Recall to Imagine以解决长期依赖问题 |
reinforcement learning world model world models |
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| 3 |
Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control |
提出深度强化学习方法以优化水库运营决策 |
reinforcement learning deep reinforcement learning DRL |
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| 4 |
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation |
提出基于重要性采样的对比持续学习方法以解决灾难性遗忘问题 |
representation learning contrastive learning distillation |
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| 5 |
Efficient Off-Policy Learning for High-Dimensional Action Spaces |
提出Vlearn以解决高维动作空间中的数据低效问题 |
reinforcement learning deep reinforcement learning policy learning |
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| 6 |
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning |
提出基于控制的图嵌入与数据增强以提升对比学习效果 |
representation learning contrastive learning |
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| 7 |
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning |
提出GPTRec以解决推荐系统中的多样性优化问题 |
reinforcement learning teacher-student |
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| 8 |
Why Online Reinforcement Learning is Causal |
提出因果模型以增强在线强化学习的效果 |
reinforcement learning offline RL |
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| 9 |
Lightweight Cross-Modal Representation Learning |
提出轻量级跨模态表示学习以解决高资源消耗问题 |
representation learning |
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| 10 |
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning |
提出RL-CFR以解决不完全信息扩展形式博弈中的动态动作抽象问题 |
reinforcement learning |
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| 11 |
Noisy Spiking Actor Network for Exploration |
提出噪声脉冲演员网络以解决深度强化学习中的探索问题 |
reinforcement learning deep reinforcement learning |
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