Density-Matrix Reinforcement Learning
arXiv:2610.17214v1 Announce Type: new Abstract: What information does a reinforcement learning policy omit? At each state, a standard policy stores one probability per action. It has no place to recor
Knowledge catalogue
arXiv:2610.17214v1 Announce Type: new Abstract: What information does a reinforcement learning policy omit? At each state, a standard policy stores one probability per action. It has no place to recor
arXiv:2602.15828v2 Announce Type: replace-cross Abstract: Learning generalist policies capable of accomplishing a wide range of everyday tasks remains an open challenge in dexterous manipulation. In p
arXiv:2610.14691v1 Announce Type: new Abstract: Recent advances in 3D hand-object interaction (HOI) perception have opened new opportunities for learning dexterous robotic skills from human videos. Ho
arXiv:2610.14372v1 Announce Type: cross Abstract: Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisi
arXiv:2610.17208v1 Announce Type: cross Abstract: Cutting a piece of food looks effortless in human hands. For a robot, however, every cut is a high-contact interaction with a deformable, elasto-plast
arXiv:2610.17789v1 Announce Type: cross Abstract: A long-horizon manipulation task consists of primitive skills and the transitions connecting them. Learning skills that can be executed in sequence re
arXiv:2610.16458v1 Announce Type: cross Abstract: Preference alignment enables language models to better reflect human values, but the preference data contains sensitive information, especially when d
arXiv:2610.15932v1 Announce Type: new Abstract: Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something convent
arXiv:2610.14473v1 Announce Type: cross Abstract: We propose a new framework for banner ad auctions, leveraging diffusion models to allow for multiple bidders to co-win a single banner ad opportunity,
arXiv:2605.06538v2 Announce Type: replace Abstract: Diffusion-based posterior samplers draw from measurement- or reward-conditioned posteriors using pretrained diffusion priors. Though widely used for
arXiv:2610.14159v1 Announce Type: cross Abstract: Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited
arXiv:2610.14571v1 Announce Type: cross Abstract: Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions. Although carbon-aware scheduling can reduce these emis
arXiv:2602.02928v2 Announce Type: replace Abstract: Modern generative models based on diffusion or flow matching transport noise toward the data manifold, without representing the data manifold explic
arXiv:2603.11634v2 Announce Type: replace Abstract: Robotics datasets for imitation learning typically consist of long-horizon trajectories of different lengths over states, actions, and high-dimensio
arXiv:2610.14064v1 Announce Type: new Abstract: Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-
arXiv:2610.18613v1 Announce Type: new Abstract: Self-distillation is an emerging approach that trains a model to imitate its own predictions when conditioned on privileged information, such as correct
arXiv:2610.18609v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal p
arXiv:2610.16618v1 Announce Type: new Abstract: Policy representations in reinforcement learning have expanded beyond Gaussian policies to diffusion and flow-matching based generative policies. Howeve
arXiv:2603.00466v2 Announce Type: replace Abstract: Despite impressive progress in video generation, existing models remain limited to surface-level plausibility, lacking a coherent and unified unders
arXiv:2610.15662v1 Announce Type: new Abstract: Generative policies offer expressive action distributions for online reinforcement learning (RL), but many diffusion- and flow-based policies require co
arXiv:2610.15112v1 Announce Type: new Abstract: High-fidelity closed-loop simulation for autonomous driving increasingly relies on dense multi-view 3D reconstruction. While recent feed-forward geometr
arXiv:2610.14624v1 Announce Type: new Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive secto
arXiv:2610.16523v1 Announce Type: cross Abstract: Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on t
arXiv:2610.17466v1 Announce Type: new Abstract: Frechet Distributional (FD) objectives provide an effective distribution-level signal for post-training generative models. For latent generative models,
arXiv:2610.16176v1 Announce Type: new Abstract: Situated 3D Question Answering requires models to interpret a 3D scene relative to an agent's pose. Existing methods commonly convey that pose through a
arXiv:2610.18822v1 Announce Type: new Abstract: Egocentric human videos offer a scalable alternative to robot teleoperation for collecting dexterous manipulation data. However, two challenges limit th
arXiv:2610.18867v1 Announce Type: new Abstract: Bringing general-purpose agents into the physical world calls for an executable representation of physical intelligence. Compared with vision-language-a
arXiv:2606.12965v2 Announce Type: replace Abstract: Scalable robot imitation learning relies on large-scale heterogeneous data from diverse robots or body-free data, making Cartesian end-effector acti
arXiv:2610.18738v1 Announce Type: new Abstract: Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response
arXiv:2506.18532v2 Announce Type: replace-cross Abstract: Grammatical Error Correction (GEC) and feedback play a vital role in supporting second language (L2) learners, educators, and examiners. While
arXiv:2506.02841v5 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms
arXiv:2610.15871v1 Announce Type: cross Abstract: We study stationary discrete-time mean-field games with finite state and action spaces under the average-reward criterion, with an entropy-regularized
arXiv:2610.18790v1 Announce Type: new Abstract: Long-term robot autonomy depends on more than effective action generation: a policy must recognize when its behavior is no longer sufficiently supported
arXiv:2602.09506v4 Announce Type: replace Abstract: Contrastive learning (CL) is a predominant technique in image classification, but they showed limited performance with an imbalanced dataset. Recent
arXiv:2610.16166v1 Announce Type: new Abstract: Interactive world models must capture how multiple participants jointly change a shared environment while observing different parts of it. Competitive g
arXiv:2610.16411v1 Announce Type: new Abstract: Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Cla
arXiv:2610.16172v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure tha
arXiv:2610.17734v1 Announce Type: new Abstract: Exploration bounds learning efficiency in reinforcement learning with verifiable rewards (RLVR). Current methods explore mostly through independent samp
arXiv:2610.18593v1 Announce Type: new Abstract: Fairness constraints in predict-then-optimize pipelines are necessarily enforced on predicted outcomes hat{y}, since true outcomes y are unobserved at d
arXiv:2610.16694v1 Announce Type: new Abstract: Adversarial detection on evolving attributed graphs faces two challenges: fraud labels arrive late or not at all, and natural distributional drift erode
arXiv:2610.15307v1 Announce Type: new Abstract: Efficient neural architecture search (NAS) requires accurate performance estimation under limited evaluation budgets. Existing zero-cost proxies (ZCPs)
arXiv:2605.05791v2 Announce Type: replace Abstract: Fitted Q-iteration (FQI) is well understood when the data come from a fixed distribution and the action set is finite, whereas value-based continuou
arXiv:2602.02481v2 Announce Type: replace Abstract: Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on different
arXiv:2610.14588v1 Announce Type: cross Abstract: Test-time sampling promises to turn one action prediction into a set of distinct executable candidates that a verifier can choose among. Downstream fi
arXiv:2610.17161v1 Announce Type: new Abstract: Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of
arXiv:2610.18118v1 Announce Type: new Abstract: Visually similar actions can produce contact loss, jamming, slippage, or irreversible damage, while tactile feedback may arrive after a critical transit
arXiv:2610.17449v1 Announce Type: cross Abstract: Last-mile functional coverage closure for modern chip designs remains costly because a mature UVM environment exposes many controls while direct hits
arXiv:2610.14067v1 Announce Type: new Abstract: Competitive humanoid table tennis requires two distinct abilities: executing a desired return and deciding where to return the ball. We separate these t
arXiv:2610.17468v1 Announce Type: cross Abstract: As the complexity of space missions increases, autonomous spacecraft guidance must accommodate transitions between mission phases with distinct dynami
arXiv:2610.16868v1 Announce Type: new Abstract: Simplicity bias, which can arise from sequential feature learning during training, has been proposed as a general explanation for the success of deep le
arXiv:2610.16364v1 Announce Type: new Abstract: Speech-driven visual grounding in remote sensing (RSVG) removes the typing bottleneck of text-based interaction and is intended to preserve prosodic int
arXiv:2610.14561v1 Announce Type: new Abstract: Most existing approaches rely on video or motion-capture data for reinforcement learning (RL) training; however, all of these methods require an upper-l
arXiv:2610.16862v1 Announce Type: new Abstract: Optical music recognition (OMR) for medieval chant lacks training data linking manuscript images to musical symbols across notational families. Using ex
arXiv:2610.14140v1 Announce Type: cross Abstract: Image-level MRI harmonization is often evaluated with a frozen site probe, a site classifier trained on raw scans and applied unchanged to harmonized
arXiv:2610.15183v1 Announce Type: new Abstract: The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the at
arXiv:2610.17174v1 Announce Type: cross Abstract: Achieving complex, long-horizon robot behavior remains challenging because successful execution depends on skills grounded in a robot's physical affor
arXiv:2610.14293v1 Announce Type: new Abstract: Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches addr
arXiv:2610.17742v1 Announce Type: cross Abstract: Dimensionality reduction can preserve the average performance of text embeddings even when a substantial fraction of their dimensions is removed. Aggr
arXiv:2601.07632v5 Announce Type: replace Abstract: Discrete motion tokenization has recently enabled Large Language Models (LLMs) to serve as versatile backbones for motion understanding and motion-l
arXiv:2610.14852v1 Announce Type: new Abstract: Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming accura