GenAI4ELab/papercli-papers huggingface dataset:
M$^3$E: Continual Vision-and-Language Navigation via Mixture of Macro and Micro Experts
Authors: Yongliang Jiang; Huaidong Zhang; Xuandi Luo; Shengfeng He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-and-Language Navigation (VLN) agents have shown strong capabilities in following natural language instructions. However, they often struggle to generalize across environments due to catastrophic forgetting, which limits their practical use in real-world settings where agents must continually adapt to new domains. We argue that overcoming forgetting across environments hinges on decoupling global scene reasoning from local perceptual alignment, allowing the agent to adapt to new domains while preserving specialized capabilities.
To this end, we propose M$^3$E, the Mixture of Macro and Micro Experts, an environment-aware hierarchical MoE framework for continual VLN. Our method introduces a dual-router architecture that separates navigation into two levels of reasoning. A macro-level, scene-aware router selects strategy experts based on global environmental features (e.g., office vs. residential), while a micro-level, instance-aware router activates perception experts based on local instruction-vision alignment for step-wise decision making. To preserve knowledge across domains, we adopt a dynamic momentum update strategy that identifies expert utility in new environments and selectively updates or freezes their parameters.
We evaluate M$^3$E in a domain-incremental setting on the R2R and REVERIE datasets, where agents learn across unseen scenes without revisiting prior data. Results show that our method consistently outperforms standard fine-tuning and existing continual learning baselines in both adaptability and knowledge retention, offering a parameter-efficient solution for building generalizable embodied agents.
Secret-Protected Evolution for Differentially Private Synthetic Text Generation
Authors: Tianze Wang; Zhaoyu Chen; Jian Du; Yingtai Xiao; Linjun Zhang; Qiang Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Text data has become extremely valuable on large language models (LLMs) and even lead to general artificial intelligence (AGI).
A lot of high-quality text in the real world is private and cannot be freely used due to privacy concerns. Therefore, differentially private (DP) synthetic text generation has been proposed, aiming to produce high-utility synthetic data while protecting sensitive information.
However, existing DP synthetic text generation imposes uniform guarantees that often overprotect non-sensitive content, resulting in substantial utility loss and computational overhead. Therefore, we propose Secret-Protected Evolution (SecPE), a novel framework that extends private evolution with secret-aware protection. 
Theoretically, we show that SecPE satisfies $(\vp, \vr)$-secret protection, constituting a relaxation of Gaussian DP that enables tighter utility–privacy trade-offs, while also substantially reducing computational complexity relative to baseline methods.
Empirically, across the OpenReview, PubMed, and Yelp benchmarks, SecPE consistently achieves lower Fréchet Inception Distance (FID) and higher downstream task accuracy than GDP-based Aug-PE baselines, while requiring less noise to attain the same level of protection. 
Our results highlight that secret-aware guarantees can unlock more practical and effective privacy-preserving synthetic text generation.
Learning to Grasp Anything By Playing with Random Toys
Authors: Dantong Niu; Yuvan Sharma; Baifeng Shi; Rachel Ding; Matteo Gioia; Haoru Xue; Henry Tsai; Konstantinos Kallidromitis; Anirudh Pai; S. Shankar Sastry; Trevor Darrell; Jitendra Malik; Roei Herzig
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Robotic manipulation policies often struggle to generalize to novel objects, limiting their real-world utility. In contrast, cognitive science suggests that children develop generalizable dexterous manipulation skills by mastering a small set of simple toys and then applying that knowledge to more complex items. Inspired by this, we study if similar generalization capabilities can also be achieved by robots. Our results indicate robots can learn generalizable grasping using randomly assembled objects that are composed from just four shape primitives: spheres, cuboids, cylinders, and rings. We show that training on these "toys" enables robust generalization to real-world objects, yielding strong zero-shot performance. Crucially, we find the key to this generalization is an object-centric visual representation induced by our proposed detection pooling mechanism. Evaluated in both simulation and on physical robots, our model achieves a 67% real-world grasping success rate on the YCB dataset, outperforming state-of-the-art approaches that rely on substantially more in-domain data. We further study how zero-shot generalization performance scales by varying the number and diversity of training toys and the demonstrations per toy. We believe this work offers a promising path to scalable and generalizable learning in robotic manipulation.
Group-Relative REINFORCE Is Secretly an Off-Policy Algorithm: Demystifying Some Myths About GRPO and Its Friends
Authors: Chaorui Yao; Yanxi Chen; Yuchang Sun; Yushuo Chen; Wenhao Zhang; Xuchen Pan; Yaliang Li; Bolin Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Off-policy reinforcement learning (RL) for large language models (LLMs) is attracting growing interest, driven by practical constraints in real-world applications, the complexity of LLM-RL infrastructure, and the need for further innovations of RL methodologies. While classic REINFORCE and its modern variants like Group Relative Policy Optimization (GRPO) are typically regarded as on-policy algorithms with limited tolerance of off-policyness, we present in this work a first-principles derivation for group-relative REINFORCE — a REINFORCE variant that uses the within-group mean reward as the baseline for advantage calculation — without assuming a specific training data distribution, showing that it admits a native off-policy interpretation. This perspective yields two general principles for adapting REINFORCE to truly off-policy settings: regularizing policy updates, and actively shaping the data distribution. Our analysis demystifies some myths about the roles of importance sampling and clipping in GRPO, unifies and reinterprets two recent algorithms — Online Policy Mirror Descent and Asymmetric REINFORCE — as regularized forms of the REINFORCE loss, and offers theoretical justification for seemingly heuristic data-weighting strategies. Our findings lead to actionable insights that are validated with extensive empirical studies, and open up new opportunities for principled algorithm design in off-policy RL for LLMs. Source code for this work is available at https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k.
Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering
Authors: Sen Zhao; Lincheng Zhou; Yue Chen; Ding Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-Augmented Generation (RAG) enhances the reasoning ability of Large Language Models (LLMs) by dynamically integrating external knowledge, thereby mitigating hallucinations and strengthening contextual grounding for structured data such as graphs. Nevertheless, most existing RAG variants for textual graphs concentrate on low-dimensional structures—treating nodes as entities (0-dimensional) and edges or paths as pairwise or sequential relations (1-dimensional), but overlook cycles, which are crucial for reasoning over relational loops. Such cycles often arise in questions requiring closed-loop inference about similar objects or relative positions. This limitation often results in incomplete contextual grounding and restricted reasoning capability. In this work, we propose Topology-enhanced Retrieval-Augmented Generation (TopoRAG), a novel framework for textual graph question answering that effectively captures higher dimensional topological and relational dependencies. Specifically, TopoRAG first lifts textual graphs into cellular complexes to model multi-dimensional topological structures. Leveraging these lifted representations, a topology-aware subcomplex retrieval mechanism is proposed to extract cellular complexes relevant to the input query, providing compact and informative topological context. Finally, a multi-dimensional topological reasoning mechanism operates over these complexes to propagate relational information and guide LLMs in performing structured, logic-aware inference. Empirical evaluations demonstrate that our method consistently surpasses existing baselines across diverse textual graph tasks.
OVSeg3R: Learn Open-vocabulary Instance Segmentation from 2D via 3D Reconstruction
Authors: Hongyang Li; Jinyuan Qu; Lei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we propose a training scheme called OVSeg3R to learn open-vocabulary 3D instance segmentation from well-studied 2D perception models with the aid of 3D reconstruction. OVSeg3R directly adopts reconstructed scenes from 2D videos as input, avoiding costly manual adjustment while aligning input with real-world applications. By exploiting the 2D to 3D correspondences provided by 3D reconstruction models, OVSeg3R projects each view's 2D instance mask predictions, obtained from an open-vocabulary 2D model, onto 3D to generate annotations for the view's corresponding sub-scene. To avoid incorrectly introduced false positives as supervision due to partial annotations from 2D to 3D, we propose a View-wise Instance Partition algorithm, which partitions predictions to their respective views for supervision, stabilizing the training process. Furthermore, since 3D reconstruction models tend to over-smooth geometric details, clustering reconstructed points into representative super-points based solely on geometry, as commonly done in mainstream 3D segmentation methods, may overlook geometrically non-salient objects. We therefore introduce 2D Instance Boundary-aware Superpoint, which leverages 2D masks to constrain the superpoint clustering, preventing superpoints from violating instance boundaries. With these designs, OVSeg3R not only extends a state-of-the-art closed-vocabulary 3D instance segmentation model to open-vocabulary, but also substantially narrows the performance gap between tail and head classes, ultimately leading to an overall improvement of +2.3 mAP on the ScanNet200 benchmark. Furthermore, under the standard open-vocabulary setting, OVSeg3R surpasses previous methods by about +7.1 mAP on the novel classes, further validating its effectiveness.
Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
Authors: Ganlin Yang; Tianyi Zhang; Haoran Hao; Weiyun Wang; Yibin Liu; Dehui Wang; Guanzhou Chen; Zijian Cai; Junting Chen; Weijie Su; Wengang Zhou; Yu Qiao; Jifeng Dai; Jiangmiao Pang; Gen Luo; Wenhai Wang; Yao Mu; Zhi Hou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (VLA) models for end-to-end robot control, few studies directly address the critical gap between upstream VLM-based reasoning and downstream VLA policy learning. In this work, we take an initial step toward bridging embodied reasoning with VLA policy learning by introducing **Vlaser** - a **V**ision-**L**anguage-**A**ction Model with **s**ynergistic **e**mbodied **r**easoning capability, which is a foundational vision-language model designed to integrate high-level reasoning with low-level control for embodied agents. Built upon the high-quality Vlaser-6M dataset, Vlaser achieves state-of-the-art performance across a range of embodied reasoning benchmarks—including spatial reasoning, embodied grounding, embodied QA, and task planning.
Furthermore, we systematically examine how different VLM initializations affect supervised VLA fine-tuning, offering novel insights into mitigating the domain shift between internet-scale pre-training data and embodied-specific policy learning data. Based on these insights, our approach achieves state-of-the-art results on the WidowX benchmark and competitive performance on the Google Robot benchmark. We will open-source the model weights, data generation pipelines, and the full dataset to support future research.
OPPO: Accelerating PPO-based RLHF via Pipeline Overlap
Authors: Kaizhuo Yan; YingJie Yu; Yifan Yu; Haizhong Zheng; Fan Lai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Proximal Policy Optimization (PPO)-based reinforcement learning from human feedback (RLHF) is a widely adopted paradigm for aligning large language models (LLMs) with human preferences. However, its training pipeline suffers from substantial inefficiencies due to sequential multi-model dependencies (e.g., reward model depends on actor outputs) and long-tail response lengths, where a few long responses straggle the stage completion. We present OPPO, a novel, lightweight, and model-agnostic PPO-based RLHF framework that improves training efficiency by overlapping pipeline execution. OPPO introduces two novel techniques: (1) Intra-step overlap, which streams upstream model outputs (e.g., actor model) in right-sized chunks, enabling the downstream model (e.g., reward) to begin prefill while the upstream continues decoding; and (2) Inter-step overlap, which adaptively overcommits a few prompts and defers long generations to future steps, mitigating tail latency without discarding partial work. OPPO integrates easily with existing PPO implementations with a lightweight wrapper. Extensive evaluations show that OPPO accelerates PPO-based RLHF training by $1.8\times$--$2.8\times$ and improves GPU utilization by $1.4\times$--$2.1\times$ without compromising training convergence.
UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
Authors: Hung-Yueh Chiang; Chi-Chih Chang; Yu-Chen Lu; Chien-Yu Lin; Kai-Chiang Wu; Mohamed S. Abdelfattah; Diana Marculescu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deploying large language models (LLMs) on mobile platforms faces significant challenges due to the limited memory and shared computational resources of the device. Resource availability may be an issue as it is directly impacted by on the current device workload, adding to the uncertainty of model deployment. We introduce UniQL, a unified post-training quantization and low-rank compression framework, with on-device configurable pruning rates for edge LLMs. UniQL is a general framework that integrates quantization and low-rank compression for Transformers, State Space Models (SSMs), and hybrid models to cater to diverse edge applications. In our proposed joint framework, we introduce an efficient structured weight-sorting that speeds up the computation by 20×, quantization-aware singular value decomposition (SVD) decompositions to minimize the quantization errors, state-aware weight sorting for SSMs, and a fused rotary embedding (RoPE) kernel for the pruned models. Our framework performs weight-sorting, fine-tuning, and quantization in the cloud in a one-shot fashion, while enabling on-device configurable pruning rates up to 35%. Our experiments show that quantized and pruned models offer a memory reduction of 4×–5.7× and a token throughput improvement of 2.7×–3.4×, maintaining accuracy within 5% of the original models at 15% pruning rates across Transformers (Llama3 and Qwen2.5), SSMs (Mamba2), and hybrid models (Nemotron-H and Bamba-v2). The code and quantized models will be released at: https://github.com/enyac-group/UniQL.
Multi-Agent Guided Policy Optimization
Authors: Yueheng Li; Guangming Xie; Zongqing Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Due to practical constraints such as partial observability and limited communication, Centralized Training with Decentralized Execution (CTDE) has become the dominant paradigm in cooperative Multi-Agent Reinforcement Learning (MARL). 
However, existing CTDE methods often underutilize centralized training or lack theoretical guarantees. 
We propose Multi-Agent Guided Policy Optimization (MAGPO), a novel framework that better leverages centralized training by integrating centralized guidance with decentralized execution. 
MAGPO uses an autoregressive joint policy for scalable, coordinated exploration and explicitly aligns it with decentralized policies to ensure deployability under partial observability. 
We provide theoretical guarantees of monotonic policy improvement and empirically evaluate MAGPO on 43 tasks across 6 diverse environments. 
Results show that MAGPO consistently outperforms strong CTDE baselines and matches or surpasses fully centralized approaches, offering a principled and practical solution for decentralized multi-agent learning.
Through the Lens of Contrast: Self-Improving Visual Reasoning in VLMs
Authors: Zhiyu Pan; Yizheng Wu; Jiashen Hua; Junyi Feng; Shaotian Yan; Bing Deng; Zhiguo Cao; Jieping Ye
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Reasoning has emerged as a key capability of large language models. In linguistic tasks, this capability can be enhanced by self-improving techniques that refine reasoning paths for subsequent fine-tuning. However, extending these language-based self-improving approaches to vision language models (VLMs) presents a unique challenge: visual hallucinations in reasoning paths cannot be effectively verified or rectified. Our solution starts with a key observation about visual contrast: when presented with a contrastive VQA pair, i.e., two visually similar images with synonymous questions, VLMs identify relevant visual cues more precisely compared with when given a single VQA sample. Motivated by this observation, we propose Visual Contrastive Self-Taught Reasoner (VC-STaR), a novel self-improving framework that leverages visual contrast to mitigate hallucinations in model-generated rationales. We collect a diverse suite of VQA datasets, curate contrastive pairs according to multi-modal similarity, and generate rationales using VC-STaR. Consequently, we obtain a new visual reasoning dataset, VisCoR-$55$K, which is then used to boost the reasoning capability of various VLMs through supervised finetuning. Extensive experiments show that VC-STaR not only outperforms existing self-improving approaches but also surpasses models finetuned on the SoTA visual reasoning datasets, demonstrating that the inherent contrastive ability of VLMs can bootstrap their own visual reasoning. The code, dataset and trained models will be released upon acceptance.
EchoMotion: Unified Human Video and Motion Generation via Dual-Modality Diffusion Transformer
Authors: Yuxiao Yang; Hualian Sheng; Sijia Cai; Jing Lin; Jiahao Wang; Bing Deng; Junzhe Lu; Haoqian Wang; Jieping Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video generation models have advanced significantly, yet they still struggle to synthesize complex human movements due to the high degrees of freedom in human articulation. This limitation stems from the intrinsic constraints of pixel-only training objectives, which inherently bias models toward appearance fidelity at the expense of learning underlying kinematic principles. To address this, we introduce EchoMotion, a framework designed to model the joint distribution of appearance and human motion, thereby improving the quality of complex human action video generation. EchoMotion extends the DiT (Diffusion Transformer) framework with a dual-branch architecture that jointly processes tokens concatenated from different modalities.
Furthermore, we propose MVS-RoPE (Motion-Video Syncronized RoPE), which offers unified 3D positional encoding for both video and motion tokens. By providing a synchronized coordinate system for the dual-modal latent sequence, MVS-RoPE establishes an inductive bias that fosters temporal alignment between the two modalities. We also propose a Motion-Video Two-Stage Training Strategy. This strategy enables the model to perform both the joint generation of complex human action videos and their corresponding motion sequences, as well as versatile cross-modal conditional generation tasks. 
To facilitate the training of a model with these capabilities, we construct \textit{HuMoVe}, a large-scale dataset of approximately 80,000 high-quality, human-centric video-motion pairs.
Our findings reveal that explicitly representing human motion is complementary to appearance, significantly boosting the coherence and plausibility of human-centric video generation. Project page at: https://yuxiaoyang23.github.io/EchoMotion-webpage/.
Property-Driven Protein Inverse Folding with Multi-Objective Preference Alignment
Authors: Junqi Liu; Xiaoyang Hou; Chence Shi; Xin Liu; Zhi Yang; Jian Tang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Protein sequence design must balance designability, defined as the ability to recover a target backbone, with multiple, often competing, developability properties such as solubility, thermostability, and expression.
Existing approaches address these properties through post hoc mutation, inference-time biasing, or retraining on property-specific subsets, yet they are target dependent and demand substantial domain expertise or careful hyperparameter tuning.
In this paper, we introduce ProtAlign, a multi-objective preference alignment framework that fine-tunes pretrained inverse folding models to satisfy diverse developability objectives while preserving structural fidelity.
ProtAlign employs a semi-online Direct Preference Optimization strategy with a flexible preference margin to mitigate conflicts among competing objectives and constructs preference pairs using in silico property predictors.
Applied to the widely used ProteinMPNN backbone, the resulting model MoMPNN enhances developability without compromising designability across tasks including sequence design for CATH 4.3 crystal structures, de novo generated backbones, and real-world binder design scenarios, making it an appealing framework for practical protein sequence design.
Draft-based Approximate Inference for LLMs
Authors: Kevin Galim; Ethan Ewer; Wonjun Kang; Minjae Lee; Hyung Il Koo; Kangwook Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Optimizing inference for long-context large language models (LLMs) is increasingly important due to the quadratic compute and linear memory cost of Transformers. Existing approximate inference methods, including key-value (KV) cache dropping, sparse attention, and prompt compression, typically rely on coarse predictions of token or KV pair importance. We unify and extend recent work by introducing a framework for approximate LLM inference that leverages small draft models to more accurately predict token and KV pair importance. We provide novel theoretical and empirical analyses justifying lookahead-based importance estimation techniques. Within this framework, we present: (i) **SpecKV**, the first method to use lookahead with a small draft model to enable precise KV cache dropping; (ii) **SpecPC**, which leverages draft model attention activations to identify and discard less important prompt tokens; and (iii) **SpecKV-PC**, a cascaded compression strategy combining both techniques. Extensive experiments on long-context benchmarks demonstrate that our methods consistently achieve higher accuracy than existing baselines while retaining the same efficiency gains in memory usage, latency, and throughput.