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版本更新 · v3arXiv:2603.18859

RewardFlow: Can Action-Level Reward Be Estimated from State-Graph Topology in Agentic RL?

Xiao Feng, Bo Han, Zhanke Zhou, Jiaqi Fan, Jiangchao Yao, Ka Ho Li, Dahai Yu, Michael Kwok-Po Ng

原文摘要

In agentic tasks such as household assistance and web shopping, large language model (LLM) agents take multiple actions before receiving a final outcome indicating success or failure. For reinforcement learning (RL), this outcome obscures individual actions' contributions: a shopping trajectory may find the right product but fail to complete the purchase. To address this credit-assignment problem, we introduce RewardFlow, which connects multiple trajectories for the same task through shared states (states grouped by task-relevant equivalence or semantic similarity). The resulting directed state graph links observed trajectory segments. Therefore, an action in a failed trajectory may reach a state from which another trajectory succeeds. It propagates reward signals backward from states where the task succeeds, assigning state potentials that decrease with shortest-path distance to success in the graph. Potential differences then reward actions that reduce this distance, bringing the agent closer to success in the graph. These estimated rewards yield action-level advantages that complement trajectory-level advantages from outcome rewards during policy optimization, without a learned process reward model or manual action-level annotations. Experiments on ALFWorld, WebShop, Sokoban, and search tasks show gains over RL baselines across model scales. With 3B policies, graph construction and reward estimation take $1.47$-$2.39$ seconds per training step on ALFWorld, WebShop, and Sokoban (Fig. 1). Code is available at https://github.com/tmlr-group/RewardFlow.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.04851

Pose Distance Fields for Humanoid Robots

Yi Gu, Yukang Gao, Yangchen Zhou, Xingyu Chen, Yixiao Feng, Mingle Zhao, Yunyang Mo, Zhaorui Wang 等 10 位作者

原文摘要

Pose and motion priors play a crucial role in humanoid robotics. Although such priors have been widely studied in human motion recovery (HMR) domain with a range of models, their adoption for humanoid robots remains limited, largely due to the scarcity of high-quality humanoid motion data. In this work, we introduce Pose Distance Fields for Humanoid Robots (PDF-HR), a lightweight prior that represents the robot pose distribution as a continuous and differentiable manifold. Given an arbitrary pose, PDF-HR predicts its distance to a large corpus of retargeted robot poses, yielding a smooth measure of pose plausibility that is well suited for optimization and control. PDF-HR can be integrated as a reward shaping term, a regularizer, or a standalone plausibility scorer across diverse pipelines. We evaluate PDF-HR on various humanoid tasks, including single-trajectory motion tracking, general motion tracking, style-based motion mimicry, and general motion retargeting. Experiments show that this plug-and-play prior consistently and substantially strengthens strong baselines. Code and models are publicly available at https://gaoyukang33.github.io/PDF-HR.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11554

Kimodo-Policy: Learning Kinematic Action Models for Humanoid Loco-Manipulation

Yunheng Wang, Jiaxi Zhang, Wei Lu, Yuetong Fang, Taowen Wang, Yixiao Feng, Shuning Zhang, Junzhe Xu 等 15 位作者

原文摘要

Humanoid loco-manipulation requires continuous coordination of high-dimensional whole-body motion to realize task objectives. While vision-language-action models and world action models transfer semantic and visual-dynamics priors to robot control, these priors do not necessarily capture the structure of robot motion. We introduce Kinematic Action Models (KAMs), a paradigm that treats whole-body motion as a direct pretraining target and uses a pretrained motion generator as the primary action-generation backbone. KAMs ground learned temporal and cross-body coordination priors through task conditioning, visual observations, and execution feedback. We present Kimodo-Policy as an initial instantiation, combining a frozen Kimodo text-to-motion backbone with lightweight ControlNet-based visual conditioning. A root-body-hand hierarchy coordinates locomotion and manipulation, while motion-history conditioning and repeated action-chunk prediction bridge motion synthesis and closed-loop control. Following large-scale loco-manipulation pretraining, the policy achieves the highest overall success among compared policies on multi-task HumanoidArena (72.14%), competitive performance on SIMPLE, and 60% mean success across eight real-world tasks, with 678M parameters (92M trainable). Ablations support the contribution of the pretrained motion backbone. Together, these results provide initial evidence for motion generation as a complementary foundation for humanoid action learning.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11500

ForeAct: Geometry-Guided Action Learning from World Representations

Wei Lu, Jiaxi Zhang, Yunheng Wang, Taowen Wang, Yuetong Fang, Zhenhui Zhang, Shuning Zhang, Zizhao Yuan 等 11 位作者

原文摘要

Robotic manipulation requires representations that capture both scene geometry and its evolution during interaction. Existing approaches have largely treated these two aspects separately. World action models capture such dynamics by coupling action prediction with future video generation, but their reliance on iterative diffusion or rectified-flow inference leads to high computational cost, while their representations lack explicit spatial structure. Explicitly modeling scene geometry typically requires auxiliary modules that increase both parameter count and computational cost. Conversely, geometry-centric policies primarily recover observed spatial structure and often lack explicit supervision for anticipating future geometric changes during interaction. To address these limitations, we introduce ForeAct, which repurposes a pretrained video world model as a single-pass representation backbone for low-latency control. ForeAct directly processes the clean latent of the current observation in one forward pass, eliminating future-frame synthesis and iterative denoising while retaining the backbone's pretrained spatiotemporal priors. Rather than introducing a separate geometry backbone, ForeAct uses learnable tokens supervised by next-frame relative depth to shape the shared video representation through bidirectional self-attention. This compact interface turns the spatial knowledge already encoded in the backbone into a future-aware representation for action generation, with little parameter overhead. The lightweight geometry readout is used only during training and removed at deployment, enabling low-latency, geometry-aware control without depth input or an external geometry backbone. Without additional large-scale pretraining, ForeAct achieves 96.6% average success on LIBERO, 63.5% on SimplerEnv-Bridge, and a 74.4% success rate across 90 real-world rollouts.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39572

RV2GC: Reconstructing the Garbling Boundary in a RISC-V Garbled Processor

Zixiao Feng, Xiaolin Li, Chenglu Jin, Wei Yan, Yong Zhang, Hongwei Liu, Yong Liu, Qinfen Hao

原文摘要

Programmable garbled-circuit execution must balance the benefits of cross-program reuse against the cost of repeated work during secure execution. A complete garbled processor provides a fixed circuit for many programs, but its evaluator repeatedly visits processor-wide structures even when only a small part performs the current protected computation. We present RV2GC, which reconstructs the garbling boundary around a fixed library of RISC-V instruction circuits. Public scheduling and operand routing operate outside the circuit topologies; persistent label-encoded state connects independently invoked garbled instruction circuits and instruction-specific garbling-free paths through a common interface. A Conditional-execution Stack and protected commits support secret-dependent branches, bounded loops, and secret-indexed memory access under public scheduling, keeping instruction and memory-address sequences independent of private inputs. Across nine benchmarks, measured ARM2GC-to-RV2GC runtime ratios range from 12.81 to 4,043. In terms of circuit size, the largest listed instruction circuit has 3,386 gates, corresponding to 2.044% and 0.393% of the 256-byte and 4,096-byte Amber processors, respectively. Additional comparisons quantify schedule storage, communication, and the cost of secret-dependent execution.

cs.CR · cs.AR

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.13ACL Anthology · ACL 2026

TaoType: Predicting Fine-Grained Typing Intent for Faster Search

Yipeng Yu, Yichen Yuan, Chengxiao Feng, Xu Liu

原文摘要

“Is the user’s current query input exactly what they intend to search for?” Our work aims to answer this question by determining, at each typing, whether the current query is complete. If so, a search is implicitly triggered in advance without waiting for user confirmation. This approach reduces response time and enhances the user search experience. Specifically, we propose TaoType, a client-side framework that introduces innovation in data sampling, feature selection, model design and training, and online strategy. Experiments in a leading mobile shopping application named Taobao validate its effectiveness, achieving offline precision/recall/accuracy of 0.7936/0.8196/0.7742, respectively, and decreasing online response time by 640.51±93.65 milliseconds, which is of great benefit to the search system. Unlike prior work that focuses on optimizing server-side engineering pipelines or simplifying ranking models, our method leverages client-side typing behavior for real-time early prediction, utilizing on-device computation to gain response time reducing. To the best of our knowledge, our work is the first to identify and address this problem. This work also introduces App Intelligence, a new paradigm for enhancing mobile applications by integrating on-device AI to boost business value and user experience.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表