cs.LG（2026-02-12）

📊 共 33 篇论文 | 🔗 3 篇有代码

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支柱二：RL算法与架构 (RL & Architecture) (20 🔗2) 支柱九：具身大模型 (Embodied Foundation Models) (9 🔗1) 支柱一：机器人控制 (Robot Control) (2) 支柱八：物理动画 (Physics-based Animation) (1) 支柱四：生成式动作 (Generative Motion) (1)

🔬 支柱二：RL算法与架构 (RL & Architecture) (20 篇)

#题目一句话要点标签🔗⭐
1 On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage 提出新的框架以解决离线强化学习中的复杂性问题 reinforcement learning offline RL offline reinforcement learning
2 Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL 提出基于度量空间学习的多模态状态估计方法，提升强化学习在噪声环境下的鲁棒性。 reinforcement learning multimodal
3 FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Client 提出FedGRPO，通过群体相对奖励在联邦学习中高效优化保护隐私的大模型。 reinforcement learning foundation model
4 In-Context Function Learning in Large Language Models 利用高斯过程视角分析大语言模型的上下文函数学习能力 reinforcement learning large language model
5 DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels DICE：扩散大语言模型擅长生成CUDA内核，性能超越自回归模型。 reinforcement learning large language model
6 TS-Memory: Plug-and-Play Memory for Time Series Foundation Models 提出TS-Memory以解决时间序列模型适应性问题 distillation foundation model
7 Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation 提出广义On-Policy蒸馏框架G-OPD，通过奖励外推提升学生模型性能，甚至超越教师模型。 reinforcement learning teacher-student distillation
8 Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards 提出基于真实超算反馈的在线强化学习方法，提升LLM生成高性能HPC代码能力 reinforcement learning large language model
9 From Path Signatures to Sequential Modeling: Incremental Signature Contributions for Offline RL 提出增量签名贡献方法，用于解决离线强化学习中时序敏感控制问题 reinforcement learning offline RL offline reinforcement learning
10 Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning 提出TAMPO，将温度控制视为元策略，自适应提升LLM强化学习效果 reinforcement learning large language model
11 Unifying Stable Optimization and Reference Regularization in RLHF 统一稳定优化与参考正则化，提升RLHF对齐效果 reinforcement learning preference learning RLHF
12 Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data 提出Flow-Guided Neural Operator自监督学习框架，提升时间序列数据表征能力 flow matching representation learning masked autoencoder
13 Mitigating Mismatch within Reference-based Preference Optimization 提出Hybrid-DPO以解决直接偏好优化中的不匹配问题 DPO direct preference optimization large language model
14 Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training 提出基于分布判别理论的On-Policy SFT框架，提升LLM泛化能力 reinforcement learning offline RL DPO ✅
15 The Observer Effect in World Models: Invasive Adaptation Corrupts Latent Physics 提出PhyIP评估协议以解决物理模型适应性问题 world model
16 How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics 研究采样策略对LLM对齐的影响，揭示迭代对齐中的稳定性和风险 direct preference optimization large language model
17 KAN-FIF: Spline-Parameterized Lightweight Physics-based Tropical Cyclone Estimation on Meteorological Satellite 提出KAN-FIF轻量级框架，用于气象卫星上基于物理的热带气旋估计。 MAE multimodal ✅
18 Improved state mixing in higher-order and block diagonal linear recurrent networks 提出高阶和块对角线性循环网络，提升长序列建模的效率与表达能力。 Mamba SSM state space model
19 RAM-Net: Expressive Linear Attention with Selectively Addressable Memory 提出RAM-Net，通过可选择寻址的显式记忆增强线性注意力的表达能力 linear attention
20 Latent-Variable Learning of SPDEs via Wiener Chaos 提出基于Wiener混沌的SPDEs潜变量学习方法，无需噪声数据即可学习随机偏微分方程。 latent dynamics spatiotemporal

🔬 支柱九：具身大模型 (Embodied Foundation Models) (9 篇)

#题目一句话要点标签🔗⭐
21 SafeNeuron: Neuron-Level Safety Alignment for Large Language Models SafeNeuron：提出神经元级别的安全对齐方法，提升大语言模型对抗神经元剪枝攻击的鲁棒性。 large language model multimodal
22 PASCAL: A Phase-Aware Scheduling Algorithm for Serving Reasoning-based Large Language Models PASCAL：一种面向推理型大语言模型的阶段感知调度算法 large language model chain-of-thought
23 Manifold-Aware Temporal Domain Generalization for Large Language Models 提出MaT-LoRA，通过流形感知的时序领域泛化方法提升LLM在时序数据上的性能。 large language model
24 SkillRater: Untangling Capabilities in Multimodal Data SkillRater：解耦多模态数据中的能力，提升视觉语言模型性能 multimodal
25 Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal Brain4FMs：脑电信号基础模型评估基准，促进可扩展和可迁移学习。 foundation model
26 It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks TIME：面向下一代时间序列预测基准，解决现有基准的数据、任务和评估局限性。 large language model foundation model ✅
27 Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing 提出加速Prompt压力测试(APST)框架，评估LLM在重复推理下的安全可靠性 large language model
28 Krause Synchronization Transformers 提出Krause注意力机制，通过局部同步缓解Transformer中的表征坍塌问题。 large language model
29 RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis RooflineBench：提出基于Roofline模型的片上LLM基准测试框架 large language model

🔬 支柱一：机器人控制 (Robot Control) (2 篇)

#题目一句话要点标签🔗⭐
30 Is Online Linear Optimization Sufficient for Strategic Robustness? 提出在线线性优化算法以提升竞标策略的鲁棒性 manipulation
31 Temporally Unified Adversarial Perturbations for Time Series Forecasting 提出时序统一对抗扰动（TUAPs）方法，提升时间序列预测模型的安全性。 manipulation

🔬 支柱八：物理动画 (Physics-based Animation) (1 篇)

#题目一句话要点标签🔗⭐
32 SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG SpaTeoGL：时空图学习用于颅内脑电癫痫起始区可解释性分析 spatiotemporal

🔬 支柱四：生成式动作 (Generative Motion) (1 篇)

#题目一句话要点标签🔗⭐
33 Learn from Your Mistakes: Self-Correcting Masked Diffusion Models 提出ProSeCo，通过自校正机制提升Masked扩散模型的生成质量与效率。 MDM

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