cs.LG(2025-04-15)
📊 共 9 篇论文 | 🔗 1 篇有代码
🎯 兴趣领域导航
支柱二:RL算法与架构 (RL & Architecture) (4 🔗1)
支柱九:具身大模型 (Embodied Foundation Models) (4)
支柱一:机器人控制 (Robot Control) (1)
🔬 支柱二:RL算法与架构 (RL & Architecture) (4 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | A Clean Slate for Offline Reinforcement Learning | 针对离线强化学习,提出统一算法框架Unifloral并优化算法实现。 | reinforcement learning TD3 offline RL | ✅ | |
| 2 | Cross-cultural Deployment of Autonomous Vehicles Using Data-light Inverse Reinforcement Learning | 提出数据轻量级逆强化学习,解决自动驾驶车辆跨文化部署问题。 | reinforcement learning inverse reinforcement learning | ||
| 3 | A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce | 提出Reinforce-Rej,一种极简的LLM推理方法,提升KL效率和稳定性。 | reinforcement learning PPO large language model | ||
| 4 | ViMo: A Generative Visual GUI World Model for App Agents | ViMo:用于App智能体的生成式视觉GUI世界模型,实现图像级别的未来App界面预测。 | world model |
🔬 支柱九:具身大模型 (Embodied Foundation Models) (4 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 5 | LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation | 提出LangTraj以解决自主车辆测试中的语言条件模拟问题 | language conditioned | ||
| 6 | Offline Learning and Forgetting for Reasoning with Large Language Models | 通过离线学习与遗忘,提升大语言模型在复杂推理问题上的效率与准确率 | large language model | ||
| 7 | Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection | XPerT:通过可解释模型选择加速设备端LLM个性化 | large language model | ||
| 8 | DataDecide: How to Predict Best Pretraining Data with Small Experiments | DataDecide:通过小规模实验预测最佳预训练数据,降低大模型训练成本 | large language model |
🔬 支柱一:机器人控制 (Robot Control) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 9 | Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models | 提出基于行为模型的全身人形机器人零样本控制方法 Meta Motivo | humanoid humanoid control reinforcement learning |