SLA: Scaling Spatial-Language-Action Model via Continual Pre-training
Fuhao Li, Wenxuan Song, Qizhou Huang, Ziyang Zhou, Jingbo Wang, Junwu E, Pengwei Wang, Yan Wang 等 10 位作者
AI 速览：SLA 从预训练 pi05 策略出发，在持续预训练与下游后训练中联合优化动作预测和视觉嵌入与冻结 3D 感知模型空间表示的对齐，以构建可迁移的空间理解能力。
Reliable robotic manipulation requires both semantic understanding and precise control in three-dimensional space. However, vision-language-action (VLA) foundations largely inherit visual representations from 2D vision-language pre-training, while spatial supervision is often introduced only during task-specific adaptation. Developing spatial understanding as a transferable foundation-model capability therefore calls for learning from broader robot experience. We present SLA, a Spatial-Language-Action Model that scales implicit spatial alignment to continual pre-training and maintains it during downstream post-training. Initialized from a pretrained pi05 policy, SLA jointly optimizes action prediction and alignment between intermediate visual embeddings and spatial representations from a frozen 3D perception model. We curate and unify heterogeneous robot datasets to support spatial learning across diverse embodiments, tasks, and scenes. To accommodate this diversity, we introduce feature-dependent gates that adaptively aggregate visual embeddings from multiple VLA layers for spatial supervision. The teacher further exploits partially available depth and camera calibration alongside RGB observations, enabling effective use of heterogeneous geometric annotations. Probabilistic state dropout encourages reliance on visual cues and mitigates overfitting to demonstration trajectories. All auxiliary components are used only during training, allowing direct reuse of pretrained VLA weights and preserving the original inference architecture.
Graph forecasting systems often rely on prediction intervals to quantify uncertainty across connected locations, but the true outcomes needed for evaluation may be only partially observed. We study how an auditor can selectively retrieve missing outcomes to determine whether there exists a connected group whose outcomes violate the same side of their respective forecast intervals. The audit stops when the observed labels determine the event or the query budget is exhausted. On three public graph time series, MOMS reduces mean queries by 19.6% to 38.5% relative to screened random acquisition when 30% of labels are hidden. Under localized masking on METR-LA, shortest witness finds more positive cases within ten queries, although the difference depends on node ordering. Direct component traversal reproduces all 9,696 screened random traces without enumerating connected subsets. The experiments show that reducing queries to a decision does not necessarily improve early event discovery or reduce local computation.
Competing speech can cause excess emissions in a connectionist temporal classification (CTC) recognizer. We penalize the amount by which the noisy student's expected collapsed token count exceeds a frozen teacher's count on the paired clean utterance. The one-sided objective caches one scalar per utterance and adds no inference module. With 147,456 trainable parameters in wav2vec 2.0, it improves competing-speech word error rate (WER) over matched CTC-only adaptation across three development seeds. On a locked LibriSpeech test, speech-interference WER falls from 45.198% to 44.323%, while clean WER changes by 0.004 percentage points. Unseen VCTK interferers yield a 0.307-point improvement. Ablations favor collapsed-count control over frame-mass regularization. Longer training increases the speech gain but worsens noise and music performance, identifying an insertion-deletion tradeoff rather than uniform robustness.
AI 速览：SCARL 通过按响应组内相对置信度与结果之间的对齐差距重塑优势值，使强化学习策略的置信度与奖励信号对齐，并缓解熵坍缩。
Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning by scoring sampled responses with a rule-based verifier and reinforcing those that pass. Where a classifier's training couples predicted probability to labels, nothing ties an RLVR policy's confidence in a response to its reward: aligned training would drive responses with the same advantage toward equal confidence, yet the advantage is blind to confidence. Under this sequence-confidence misalignment, a success the policy barely found earns the same update as one it produces with near certainty, and a confident error is corrected no harder than an exploratory one. We propose SCARL (Sequence Confidence-Aligned Reinforcement Learning), which reshapes, but never re-signs, each advantage with the alignment gap between the response's group-relative confidence and the outcome its advantage certifies, normalized into shares of each class's advantage budget. Misaligned samples absorb the force, responses with the same advantage are driven toward equal confidence, and repairing the misalignment doubles as a remedy for the entropy collapse that erodes exploration. SCARL provably reduces to its base estimator when a class's confidences agree, adds no extra passes and one hyperparameter. On Qwen3-4B-Base it lifts average Pass@32 by 2.4 points over DAPO and is best among nine compute-matched methods on both coverage and per-draw accuracy; applied unchanged across four estimators and four backbones, it improves 14 of 16 settings while preserving policy entropy and widening the confidence-correctness separation. Our code is available at https://github.com/kezhh163/scarl.
Yueqi Zhu, Yan Pan, Chufan Rui, Jiasheng Luo, Bo Zhou
In safety-critical scenarios, the protection level of the autonomous navigation system is crucial for enabling mobile robots to perform safe tasks. However, existing studies on probabilistic navigation systems for robots usually perform offline accuracy evaluations using limited datasets and assume that the results can be applied to unknown real-world environments. As a result, current autonomous mobile robots often lack protection levels for online safety assessment. To fill this gap, we propose a safety-critical LiDAR-inertial odometry (LIO) that provides deterministic protection levels based on a novel on-manifold set-membership filter. By adopting the unknown but bounded assumption, we derive a neat closed-form relationship between point cloud noise and the uncertainty of the estimation from the iterative closest point algorithm. Using this relationship, we design an on-manifold ellipsoidal set-membership filter (OM-ESMF) and implement it within the LIO system. Leveraging the properties of the set-membership filter, our system offers the feasible sets of the estimated locations as the deterministic protection levels, serving as safety references for the robots' downstream autonomous operations. The experimental results show that our system can provide effective deterministic online safety references for diverse robots in various environments.
Social annotation platforms such as Perusall and Hypothesis are widely used to support student engagement with course readings, often through required commenting. Yet visible participation does not necessarily produce sustained or substantive peer exchange. Analyzing six weeks of Perusall activity from a graduate course (2,873 comments), we found that participation was often fragmented: 53.5% of comments received no reply, 38.3% were posted within the final 12 hours before the deadline, and over one-third of a double-coded sample went no further than repeating or agreeing with the reading or a peer. Interviews pointed to two linked difficulties: locating a discussion worth entering and contributing something that meaningfully advances it. We present Lookover, an AI-assisted annotation system that scaffolds both stages without writing for students. Discussion Overview helps students identify active themes and relevant threads, while Spark Thinking poses questions that encourage them to move beyond reaction and restatement toward questioning, extending, and substantively responding to peers. A preliminary study with five participants found Discussion Overview valuable for those who noticed it, while the usefulness of Spark Thinking depended on how much a participant already knew about the topic.
Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition conditions. We propose AnyCXR, a unified framework that enables generalizable multi-organ segmentation across arbitrary CXR projection angles using only synthetic supervision. The method combines a Multi-stage Domain Randomization (MSDR) engine, which generates over 100,000 anatomically faithful and highly diverse synthetic radiographs from 3D CT volumes, with a Conditional Joint Annotation Regularization (CAR) learning strategy that leverages partial and imperfect labels by enforcing anatomical consistency in a latent space. Trained entirely on synthetic data, AnyCXR achieves strong zero-shot generalization on multiple real-world datasets, providing accurate delineation of 54 anatomical structures in PA, lateral, and oblique views. The resulting segmentation maps support downstream clinical tasks, including automated cardiothoracic ratio estimation, spine curvature assessment, and disease classification, where the incorporation of anatomical priors improves diagnostic performance. These results demonstrate that AnyCXR establishes a scalable and reliable foundation for anatomy-aware CXR analysis and offers a practical pathway toward reducing annotation burdens while improving robustness across diverse imaging conditions.
Wenhao Xu, Yanheng Mai, Yifei Fu, Nan Zheng, Yanbo Zhou, Zirui Huang, Mingsheng Ling, Xinjue Wang 等 9 位作者
Humanoid parkour requires robots to reason about future footholds and whole-body motions over highly discontinuous terrain. We revisit the information asymmetry in asymmetric actor-critic learning for perceptive humanoid locomotion: the critic exploits privileged simulator signals during training, whereas the deployable actor operates solely from onboard perception. We argue that the key is not exposing privileged information to the actor, but learning a deployable approximation of it to narrow the information gap between training and deployment. We present HC2Robot, a deployable perceptive humanoid parkour framework consisting of a Predicted Foresight Module (PFM) and a Hyper-Connection (HC) Transformer. PFM predicts from deployable observations the policy-conditioned timing, location, and support quality of each foot's next touchdown event, converting privileged supervision into a deployable latent representation that provides the actor with predicted touchdown information without privileged runtime sensing. The HC Transformer further enhances temporal reasoning through learnable multi-stream routing, while a future motion prediction head provides short-horizon motion priors for regularization. Across stairs, gaps, high platforms, and slopes, HC2Robot achieves a mean simulated traversal success rate of 97.60% and consistently higher safe foothold rates than strong perceptive locomotion baselines, supporting improved traversal performance and geometric foothold support under deployable sensing.
Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional modular Interactive Voice Response (IVR) systems suffer from error accumulation and high maintenance overhead. We present DuIVRS-2, a large language model (LLM)-based end-to-end framework designed for large-scale POI attribute acquisition at Baidu Maps. To address the long-tail distribution of real-world interactions, our methodology first employs a finite state machine (FSM)-guided data augmentation strategy to synthesize a balanced and diverse training dataset. We then streamline dialogue management via a selective generation scheme combined with a Chain-of-Thought (CoT) mechanism, which ensures output stability and effectively eliminates hallucinations in industrial settings. To facilitate continuous policy refinement with minimal manual effort, we design a cooperative iterative learning framework that leverages a dual-evaluator voting system. Deployed in production for two months, DuIVRS-2 processed 0.4 million calls daily and achieved a 83.9% Task Success Rate (TSR), outperforming its predecessor by 4 percentage points while maintaining a low reaction time of 130ms. This work provides a production-proven reference for developing robust, cost-effective LLM agents for large-scale industrial dialogue applications.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表
Zefang Zong Tencent Inc., Dingwei Chen SUN YAT-SEN UNIVERSITY, Yang Li Tencent Inc., Qi Yi University of Science and Technology of China, Bo Zhou, Chengming Li Shenzhen MSU-BIT University, BO Qian Tencent Inc., Peng Chen 等 9 位作者
LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post-training paradigm to further refine these capabilities. In this paper, we present AT²PO (Agentic Turn-based Policy Optimization via Tree Search), a unified framework for multi-turn agentic RL that addresses three core challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. AT²PO introduces a turn-level tree structure that jointly enables Entropy-Guided Tree Expansion for strategic exploration and Turn-wise Credit Assignment for fine-grained reward propagation from sparse outcomes. Complementing this, we propose Agentic Turn-based Policy Optimization, a turn-level learning objective that aligns policy updates with the natural decision granularity of agentic interactions. ATPO is orthogonal to tree search and can be readily integrated into any multi-turn RL pipeline. Experiments across seven benchmarks demonstrate consistent improvements over the state-of-the-art baseline by up to 1.84 percentage points in average, with ablation studies validating the effectiveness of each component.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jian Xiong Fudan University, Jingbo Zhou, Jingyong Ye, Qiang Huang, Dejing Dou Fudan University
Reinforcement learning (RL) has emerged as an effective approach for enhancing the reasoning capabilities of large language models (LLMs), especially in scenarios where supervised fine-tuning (SFT) falls short due to limited chain-of-thought (CoT) data. Among RL-based post-training methods, group relative advantage estimation, as exemplified by Group Relative Policy Optimization (GRPO), has attracted considerable attention for eliminating the dependency on the value model, thereby simplifying training compared to traditional approaches like Proximal Policy Optimization (PPO). However, existing group relative advantage estimation method still suffers from training inefficiencies, particularly when the estimated advantage approaches zero. To address this limitation, we propose Advantage-Augmented Policy Optimization (AAPO), a novel RL algorithm that optimizes the cross-entropy (CE) loss using advantages enhanced through a margin-based estimation scheme. This approach effectively mitigates the inefficiencies associated with group relative advantage estimation. Experimental results on multiple mathematical reasoning benchmarks and model series demonstrate the superior performance of AAPO. Code is available at https://github.com/JianxXiong/AAPO.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu Tencent, Ken Deng, Yuanxing Zhang Kuaishou- 快手科技, Jiaheng Liu Nanjing University, Bo Zhou
While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate–diagnose–refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent employs an MLLM-in-the-loop to serve as a visual critic, evaluating code via screenshots and providing actionable feedback. Crucially, we enforce a strict zero-reward policy for invalid renders to guarantee renderability and mitigate reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training–inference decoupling.
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cs.CL · cs.AI · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zuolong Li University of Science and Technology of China, Pingyu Wu University of Science and Technology of China, Xianwen Huang, Tianyi Wei Microsoft, Wenbo Zhou University of Science and Technology of China
Role-playing agents (RPAs) have made significant strides in mimicking static character identities. However, their personality simulations remain superficial, lacking a profound understanding of complex human psychological mechanisms. We identify a critical bottleneck termed “Personality Inertia”—a behavioral rigidity where RLHF-induced alignment bias traps models in a sanitized, “helpful assistant” persona. This inertia prevents models from adapting to diverse social contexts or expressing essential but negative traits under pressure. To bridge this gap, we propose PD-LLM, a situation-aware framework grounded in Trait Activation Theory. PD-LLM introduces Bipolar Latent Decomposition, which decouples personality traits into bidirectional LoRA adapters. These adapters are dynamically modulated by a situation-aware module based on the DIAMONDS taxonomy, allowing for precise behavioral regulation. Empirical results show that while baseline methods fail to synchronize multidimensional traits under pressure, PD-LLM achieves superior performance in both static fidelity and dynamic adaptability. By advancing from prompt engineering to intrinsic parameter control, PD-LLM effectively overcomes personality rigidity, facilitating the creation of vivid and psychologically consistent agents.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Recent progress in large language models (LLMs) is largely driven by scaling training compute through either pre-training with next-token prediction (NTP) or post-training with reinforcement learning (RL). The former contributes to learning broad knowledge and skills from general data, while struggling with data inefficiency and catastrophic forgetting in continual learning settings. The latter incentivizes reasoning capabilities with strong generalization, but is constrained by limited data availability due to its reliance on human annotation. To alleviate these issues, we propose Reinforcement Learning on Pre-Training data (RLPT), which combines the advantages of learning from general data and RL. In particular, RLPT derives reward signals directly from general text data through a next-segment reasoning objective, rewarding the policy for correctly predicting next text segments conditioned on the prefix text. Experiments across multiple benchmarks and models demonstrate the effectiveness of . For example, RLPT yields substantial improvements in continual pre-training (+4.6\%) and provides a strong foundation for post-training (+3.4\%) on Qwen3-8B-Base.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Bo Zhou, Xikang Chen, Yan Gong Zhejiang University, Yin Zhang Zhejiang University
Generating SVG-based fonts requires Multimodal Large Language Models (MLLMs) to translate high-level linguistic intent into low-level, topologically constrained symbolic sequences. However, current approaches struggle with two fundamental misalignments: the semantic ambiguity of unstructured natural language for precise geometric control, and the inefficiency of generic text tokenizers, which fragment coordinate-dense SVG XML into excessively long sequences with low information density. In this work, we propose Vector Calligrapher, a system that treats SVG generation as a conditional language modeling task optimized for both semantic grounding and representational efficiency.To bridge the semantic gap, we introduce a structured linguistic supervision Font Description Framework that decomposes typographic style into interpretable linguistic dimensions (e.g., historical lineage, affective metaphors), providing structured supervision aligned with the compositional syntax of SVG. To address the tokenization bottleneck, we design a scalable separated-coordinate strategy that bypasses the vocabulary explosion of flattened tokens while significantly compressing sequence length. Supported by VectorFont, a dataset of over 10 million hierarchically annotated glyphs, our approach improves CLIP score by +23%, reduces geometric error by ≈48%, and boosts generation efficiency by achieving an 18% Command-per-Token (C/T) ratio—a 6× increase in information density over standard baselines. These results demonstrate that combining structured linguistic supervision with efficient symbolic tokenization is essential for reliable, controllable vector graphics synthesis. VectorFont dataset, Code and model weights will be publicly released.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zihan Chang, Shuibing He Zhejiang University, Bo Zhou, Sheng Xiao Zhejiang University, Siling Yang, Rui Wang Zhejiang University, Zhe Pan
In response to the increasing demand for largescale machine learning training jobs, many organizations have deployed GPU clusters across geographically distributed regions. However, existing ILP- or genetic-based cross-cluster training approaches largely overlook the topology of decentralized clusters, lacking both topologyaware task scheduling mechanisms and automated model parallelization strategies. As a result, naively applying these optimization-based methods in cross-cluster settings leads to prohibitive scheduling overhead, due to the drastically enlarged search space induced by complex inter-cluster topologies. To address these challenges, we propose SpiderFlow, a topologyaware scheduling system specifically designed for decentralized GPU clusters. We formulate cross-cluster task scheduling as a graph optimization problem and introduce SpinSearch, a low-overhead topology-aware scheduling algorithm. In addition, for automated model parallelization, we propose TPA, a two-level scheduling framework that combines heuristic methods at the inter-cluster level with ILP-based optimization within clusters, effectively reducing the search space while maintaining high training throughput with substantially lower scheduling overhead. We evaluate SpiderFlow on a physical platform comprising 8 decentralized clusters, as well as on a simulation platform with up to 64 decentralized clusters. Experimental results demonstrate that SpiderFlow reduces job completion time (JCT) by 1.2-1.3×, improves throughput by 1.12-1.25×, and reduces scheduling overhead by 20-90× on average compared to state-of-the-art scheduling systems.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Kun Li, Zenan Xu, Junan Li, Zengrui Jin Tsinghua University, Jinghao Deng Tencent PCG, Zexuan Qiu The Chinese University of Hong Kong, Bo Zhou
Tool-Integrated Reasoning has emerged as a key paradigm to augment Large Language Models (LLMs) with computational capabilities, yet integrating tool-use into long Chain-of-Thought (long CoT) remains underexplored, largely due to the scarcity of training data and the challenge of integrating tool-use without compromising the model’s intrinsic long-chain reasoning. In this paper, we introduce DART (Discovery And Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees), a reinforcement learning framework that enables spontaneous tool-use during long CoT reasoning without additional human annotation. DART operates by constructing dynamic rollout trees during training to discover valid tool-use opportunities, branching out at promising positions to explore tool-integrated trajectories. Subsequently, a tree-based process advantage estimation identifies and credits specific sub-trajectories where tool invocation positively contributes to the solution, effectively reinforcing these beneficial behaviors during training. Extensive experiments on challenging benchmarks like AIME and GPQA-Diamond demonstrate that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Reasoning segmentation requires models to ground complex, implicit textual queries into precise pixel-level masks. Existing approaches rely on a single segmentation token , whose hidden state implicitly encodes both semantic reasoning and spatial localization, limiting the model’s ability to explicitly disentangle what to segment from where to segment. We introduce AnchorSeg, which reformulates reasoning segmentation as a structured conditional generation process over image tokens, conditioned on language grounded query banks. Instead of compressing all semantic reasoning and spatial localization into a single embedding, AnchorSeg constructs an ordered sequence of query banks: latent reasoning tokens that capture intermediate semantic states, and a segmentation anchor token that provides explicit spatial grounding. We model spatial conditioning as a factorized distribution over image tokens, where the anchor query determines localization signals while contextual queries provide semantic modulation. To bridge token-level predictions and pixel-level supervision, we propose Token–Mask Cycle Consistency (TMCC), a bidirectional training objective that enforces alignment across resolutions. By explicitly decoupling spatial grounding from semantic reasoning through structured language grounded query banks, AnchorSeg achieves state-of-the-art results on ReasonSeg test set (67.7% gIoU and 68.1% cIoU). All code and models are publicly available at https://github.com/rui-qian/AnchorSeg.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表