GenAI4ELab/papercli-papers huggingface dataset:
GenCP: Towards Generative Modeling Paradigm of Coupled physics
Authors: Tianrun Gao; Haoren Zheng; Wenhao Deng; Haodong Feng; Tao Zhang; Ruiqi Feng; Qianyi Chen; Tailin Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world physical systems are inherently complex, often involving the coupling of multiple physics, making their simulation both highly valuable and challenging. Many mainstream approaches face challenges when dealing with decoupled data. Besides, they also suffer from low efficiency and fidelity in strongly coupled spatio-temporal physical systems. Here we propose GenCP, a novel and elegant generative paradigm for coupled multiphysics simulation. By formulating coupled-physics modeling as a probability modeling problem, our key innovation is to integrate probability density evolution in generative modeling with iterative multiphysics coupling, thereby enabling training on data from decoupled simulation and inferring coupled physics during sampling. We also utilize operator-splitting theory in the space of probability evolution to establish error controllability guarantees for this “conditional-to-joint” sampling scheme. We evaluate our paradigm on a synthetic setting and three challenging multiphysics scenarios to demonstrate both principled insight and superior application performance of GenCP. Code is available at this repo: https://github.com/AI4Science-WestlakeU/GenCP.
WALT: Web Agents that Learn Tools
Authors: Viraj Prabhu; Yutong Dai; Matthew Fernandez; Krithika Ramakrishnan; Jing Gu; Yanqi Luo; silvio savarese; Caiming Xiong; Junnan Li; Zeyuan Chen; Ran Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Web agents promise to automate complex browser tasks, but current methods remain brittle -- relying on step-by-step UI interactions and heavy LLM reasoning that break under dynamic layouts and long horizons. Humans, by contrast, exploit website-provided functionality through high-level operations like search, filter, and sort. We introduce WALT (Web Agents that Learn Tools), a framework that reverse-engineers latent website functionality into deterministic, callable tools. Rather than hypothesizing ad-hoc skills, WALT exposes robust implementations of automations already designed into websites, spanning discovery (search, filter, sort), communication (post, comment, upvote), and content management (create, edit, delete). Tools abstract away low-level execution: instead of reasoning about how to click and type, agents simply call search(query) or create(listing). This shifts the computational burden from fragile step-by-step reasoning to reliable tool invocation. On VisualWebArena and WebArena, WALT achieves state-of-the-art success rates (52.9% on VisualWebArena, 50.1% on WebArena) with fewer steps and less LLM-dependent reasoning. On Online-Mind2Web, a benchmark of 139 real-world websites, WALT autonomously discovers 252 tools and improves success rate by 20.5% over a tool-free baseline, establishing a robust and generalizable paradigm for browser automation. Code: https://github.com/SalesforceAIResearch/WALT
Direct Reward Fine-Tuning on Poses for Single Image to 3D Human in the Wild
Authors: Seunguk Do; Minwoo Huh; Joonghyuk Shin; Jaesik Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Single-view 3D human reconstruction has achieved remarkable progress through the adoption of multi-view diffusion models, yet the recovered 3D humans often exhibit unnatural poses. This phenomenon becomes pronounced when reconstructing 3D humans with dynamic or challenging poses, which we attribute to the limited scale of available 3D human datasets with diverse poses. To address this limitation, we introduce DrPose, Direct Reward fine-tuning algorithm on Poses, which enables post-training of a multi-view diffusion model on diverse poses without requiring expensive 3D human assets. DrPose trains a model using only human poses paired with single-view images, employing a direct reward fine-tuning to maximize PoseScore, which is our proposed differentiable reward that quantifies consistency between a generated multi-view latent image and a ground-truth human pose. This optimization is conducted on DrPose15K, a novel dataset that was constructed from an existing human motion dataset and a pose-conditioned video generative model. Constructed from abundant human pose sequence data, DrPose15K exhibits a broader pose distribution compared to existing 3D human datasets. We validate our approach through evaluation on conventional benchmark datasets, in-the-wild images, and a newly constructed benchmark, with a particular focus on assessing performance on challenging human poses. Our results demonstrate consistent qualitative and quantitative improvements across all benchmarks. Project page: https://seunguk-do.github.io/drpose.
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning
Authors: Yuhao Wu; Yushi Bai; Zhiqiang Hu; Roy Ka-Wei Lee; Juanzi Li
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Ultra-long generation by large language models (LLMs) is a widely demanded scenario, yet it remains a significant challenge due to their maximum generation length limit and overall quality degradation as sequence length increases. Previous approaches, exemplified by LongWriter, typically rely on ''teaching'', which involves supervised fine-tuning (SFT) on synthetic long-form outputs. However, this strategy heavily depends on synthetic SFT data, which is difficult and costly to construct, often lacks coherence and consistency, and tends to be overly artificial and structurally monotonous. In this work, we propose an incentivization-based approach that, starting entirely from scratch and without relying on any annotated or synthetic data, leverages reinforcement learning (RL) to foster the emergence of ultra-long, high-quality text generation capabilities in LLMs. We perform RL training starting from a base model, similar to R1-Zero, guiding it to engage in reasoning that facilitates planning and refinement during the writing process. To support this, we employ specialized reward models that steer the LLM towards improved length control, writing quality, and structural formatting. Experimental evaluations show that our LongWriter-Zero model, trained from Qwen2.5-32B, consistently outperforms traditional SFT methods on long-form writing tasks, achieving state-of-the-art results across all metrics on WritingBench and Arena-Write, and even surpassing 100B+ models such as DeepSeek R1 and Qwen3-235B.
Empowering Small VLMs to Think with Dynamic Memorization and Exploration
Authors: Jiazhen Liu; Yuchuan Deng; Long Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Small-scale Vision-Language Models (SVLMs) are exceptionally well-suited for proprietary tasks. Equipping them with thinking capabilities is a critical step to enhance their performance and reliability in these specific domains. However, existing training paradigms, including Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Reward (RLVR), impose substantial demands on the base VLM, exceeding the capacity of SVLMs. Consequently, directly applying these paradigms to SVLMs fails to instill the desired thinking abilities. A natural solution is to combine SFT and RLVR, leveraging their complementarity to reduce the dependence on model capacity. Yet the core challenge lies in managing the inherent trade-off: excessive reliance on SFT can force the model to memorize pseudo thinking traces, while over-emphasizing RLVR can lead to unstable exploration (i.e., advantage collapse). To address this, we propose DyME, a novel training paradigm that Dynamically selects between Memorization (via SFT) and Exploration (via RLVR) at each optimization step. By ensuring that every update contributes to the trade-off, DyME serves as a robust, standalone strategy that stabilizes SVLM learning. Complementing this paradigm, we further introduce a synergistic Visual Supervision mechanism (comprising a visual checker and refiner) designed to inject dynamically enhanced, image-grounded guidance during optimization. Extensive experiments across diverse domains demonstrate that DyME consistently achieves this balance, and thus delivers substantial performance improvements on specialized tasks. These results establish DyME as a practical and effective solution for empowering SVLMs with reliable thinking capabilities.
RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data
Authors: Peiyan Hu; Haodong Feng; Hongyuan Liu; Tongtong Yan; Wenhao Deng; Tianrun Gao; Rong Zheng; Haoren Zheng; Chenglei Yu; Chuanrui Wang; Kaiwen Li; Zhi-Ming Ma; Dezhi Zhou; Xingcai Lu; Dixia Fan; Tailin Wu
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Predicting the evolution of complex physical systems remains a central problem in science and engineering. Despite rapid progress in scientific Machine Learning (ML) models, a critical bottleneck is the lack of expensive real-world data, resulting in most current models being trained and validated on simulated data. Beyond limiting the development and evaluation of scientific ML, this gap also hinders research into essential tasks such as sim-to-real transfer. We introduce RealPDEBench, the first benchmark for scientific ML that integrates real-world measurements with paired numerical simulations. RealPDEBench consists of five datasets, three tasks, nine metrics, and ten baselines. We first present five real-world measured datasets with paired simulated datasets across different complex physical systems. We further define three tasks, which allow comparisons between real-world and simulated data, and facilitate the development of methods to bridge the two. Moreover, we design nine evaluation metrics, spanning data-oriented and physics-oriented metrics, and finally benchmark ten representative baselines, including state-of-the-art models, pretrained PDE foundation models, and a traditional method. Experiments reveal significant discrepancies between simulated and real-world data, while showing that pretraining with simulated data consistently improves both accuracy and convergence. In this work, we hope to provide insights from real-world data, advancing scientific ML toward bridging the sim-to-real gap and real-world deployment. Our benchmark, datasets, and instructions are available at https://realpdebench.github.io/.
Squeeze the Soaked Sponge: Efficient Off-policy RFT for Large Language Model
Authors: Jing Liang; Jinyi Liu; Yi Ma; Hongyao Tang; YAN ZHENG; Shuyue Hu; LEI BAI; Jianye HAO
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning (RL) has demonstrated its potential to improve the reasoning ability of Large Language Models (LLMs), yet most existing Reinforcement Finetuning (RFT) methods are inherently *on-policy* RL, failing to reuse historical data and thus preventing efficient scaling. In this work, we explore the potential of *off-policy* RL to leverage historical data for rollout-efficient RFT. Specifically, we propose **Re**incarnating **Mix**-policy Proximal Policy Gradient (**ReMix**), which enables on-policy RFT methods to leverage off-policy data. ReMix consists of three major components: (1) Mix-policy proximal policy gradient with an increased Update-To-Data (UTD) ratio that utilizes the data from both current and past policies for efficient training; (2) KL-Convex policy constraint that combines the KL constraints on the base and precedent model to balance stability and flexibility; (3) Policy reincarnation that replaces the base model with the mix-policy RFT model in the mid way of training and restarts on-policy training, to achieve a seamless transition from early efficiency to steady convergence. In our experiments, we train a series of ReMix models based on PPO, GRPO from 1.5B, 7B base models. On five math reasoning benchmarks (i.e., AIME'24, AMC'23, Minerva, OlympiadBench, and MATH500), ReMix achieves an average Pass@1 accuracy of **52.10%** (with **0.079M rollouts**) and **64.39%** (with **0.011M rollouts**) on 1.5B and 7B models, respectively. Compared with 15 recent advanced models, ReMix shows SOTA-level performance with an over **30x to 450x reduction in training cost in terms of rollout data volume**, demonstrating superior training efficiency. Additionally, our multifaceted analysis reveals insightful findings, including the implicit preference for shorter responses of off-policy RFT, the collapse mode of self-reflection under severe off-policyness, etc. The code and the trained models are available at https://anitaleungxx.github.io/ReMix/ .
RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
Authors: Chunyu Miao; Henry Peng Zou; Yangning Li; Yankai Chen; Yibo Wang; Fangxin Wang; Yifan Li; Wooseong Yang; Bowei He; Xinni Zhang; Dianzhi Yu; Hanchen Yang; Hoang H Nguyen; Yue Zhou; Jie Yang; Jizhou Guo; Wenzhe Fan; Chin-Yuan Yeh; Panpan Meng; Liancheng Fang; Jinhu Qi; Wei-Chieh Huang; Zhengyao Gu; Yuwei Han; Langzhou He; Yuyao Yang; Xue Liu; Irwin King; Philip S. Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing works largely adopt one-shot settings, ignoring the iterative and feedback-driven nature of realistic workflows of scientific research development. To address this gap, we present RECODE-H, a benchmark of 102 tasks from research papers and repositories that evaluates LLMs through multi-turn interactions with human feedback. It includes structured instructions, unit tests, and a five-level feedback hierarchy to reflect realistic researcher–agent collaboration. We further present ReCodeAgent, a framework that integrates feedback into iterative code generation. Experimentswith leading LLMs, including GPT-5, Claude-Sonnet-4, DeepSeek-V3.1, and Gemini 2.5, show substantial performance gains with richer feedback, while also highlighting ongoing challenges in the generation of complex research code. RECODE-H establishes a foundation for developing adaptive, feedback-driven LLM agents in scientific research implementation.
Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs
Authors: Feng Hong; Geng Yu; Yushi Ye; Haicheng Huang; Huangjie Zheng; Ya Zhang; Yanfeng Wang; Jiangchao Yao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Large Language Models (DLLMs) have emerged as a compelling alternative to Autoregressive models, designed for fast parallel generation. However, existing DLLMs are plagued by a severe quality-speed trade-off, where faster parallel decoding leads to significant performance degradation. We attribute this to the irreversibility of standard decoding in DLLMs, which is easily polarized into the wrong decoding direction along with early error context accumulation. To resolve this, we introduce Wide-In, Narrow-Out (WINO), a training-free decoding algorithm that enables revokable decoding in DLLMs. WINO employs a parallel draft-and-verify mechanism, aggressively drafting multiple tokens while simultaneously using the model's bidirectional context to verify and re-mask suspicious ones for refinement. Verified in open-source DLLMs like LLaDA and MMaDA, WINO is shown to decisively improve the quality-speed trade-off. For instance, on the GSM8K math benchmark, it accelerates inference by 6 while improving accuracy by 2.58%; on Flickr30K captioning, it achieves a 10 speedup with higher performance. More comprehensive experiments are conducted to demonstrate the superiority and provide an in-depth understanding of WINO.
Improving Diffusion Models for Class-imbalanced Training Data via Capacity Manipulation
Authors: Feng Hong; Jiangchao Yao; Yifei Shen; Dongsheng Li; Ya Zhang; Yanfeng Wang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: While diffusion models have achieved remarkable performance in image generation, they often struggle with the imbalanced datasets frequently encountered in real-world applications, resulting in significant performance degradation on minority classes. In this paper, we identify model capacity allocation as a key and previously underexplored factor contributing to this issue, providing a perspective that is orthogonal to existing research. Our empirical experiments and theoretical analysis reveal that majority classes monopolize an unnecessarily large portion of the model's capacity, thereby restricting the representation of minority classes. To address this, we propose Capacity Manipulation (CM), which explicitly reserves model capacity for minority classes. Our approach leverages a low-rank decomposition of model parameters and introduces a capacity manipulation loss to allocate appropriate capacity for capturing minority knowledge, thus enhancing minority class representation. Extensive experiments demonstrate that CM consistently and significantly improves the robustness of diffusion models on imbalanced datasets, and when combined with existing methods, further boosts overall performance.
A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models
Authors: Junjie Zhang; Guozheng Ma; Shunyu Liu; Haoyu Wang; Jiaxing Huang; Ting-En Lin; Fei Huang; Yongbin Li; Dacheng Tao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful learn-to-reason paradigm for large reasoning models to tackle complex tasks. 
However, the current RLVR paradigm is still not efficient enough, as it works in a trial-and-error manner. To perform better, the model needs to explore the reward space by numerously generating responses and learn from fragmented reward signals, blind to the overall reward patterns.
Fortunately, verifiable rewards make the natural language description of the reward function possible, and meanwhile, LLMs have demonstrated strong in-context learning ability.
This motivates us to explore if large reasoning models can benefit from a **motivation** of the task, *i.e.*, awareness of the reward function, during the reinforcement finetuning process, as we humans sometimes do when learning.
In this paper, we introduce ***M**otivation-**e**nhanced **R**einforcement **F**inetuning* (**MeRF**), an intuitive yet effective method enhancing reinforcement finetuning of LLMs by involving *''telling LLMs rules of the game''*. 
Specifically, **MeRF** directly injects the reward specification into the prompt, which serves as an in-context motivation for the model to be aware of the optimization objective. 
This simple modification leverages the in-context learning ability of LLMs, aligning generation with optimization, thereby incentivizing the model to generate desired outputs from both inner motivation and external reward. 
Empirical evaluations demonstrate that **MeRF** achieves substantial performance gains over the RLVR baseline. 
Moreover, ablation studies show that MeRF performs better with greater consistency between the in-context motivation and the external reward function, while the model also demonstrates an ability to adapt to misleading motivations through reinforcement finetuning.
ScaleCap: Scalable Image Captioning via Dual-Modality Debiasing
Authors: Long Xing; Qidong Huang; Xiaoyi Dong; Pan Zhang; Yuhang Zang; Yuhang Cao; Jinsong Li; Shuangrui Ding; Weiming Zhang; Nenghai Yu; Jiaqi Wang; Feng Wu; Dahua Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper presents ScaleCap, a scalable image captioning strategy that generates
comprehensive and detailed image captions. The key challenges of high-quality
image captioning lie in the inherent biases of LVLMs: multimodal bias resulting in
imbalanced descriptive granularity, offering detailed accounts of some elements
while merely skimming over others; linguistic bias leading to hallucinated de-
scriptions of non-existent objects. To address these issues, we propose a scalable
debiased captioning strategy, which continuously enriches and calibrates the caption
with increased inference budget. Specifically, we propose two novel components:
heuristic question answering and contrastive sentence rating. The former generates
content-specific questions based on the image and answers them to progressively
inject relevant information into the caption. The latter employs sentence-level
offline contrastive decoding to effectively identify and eliminate hallucinations
caused by linguistic biases. With increased inference cost, more heuristic questions
are raised by ScaleCap to progressively capture additional visual details, generating
captions that are more accurate, balanced, and informative. Extensive modality
alignment experiments demonstrate the effectiveness of ScaleCap. Annotating
450K images with ScaleCap and using them for LVLM pretraining leads to consis-
tent performance gains across 11 widely used benchmarks. Furthermore, ScaleCap
showcases superb richness and fidelity of generated captions with two additional
tasks: replacing images with captions in VQA task, and reconstructing images
from captions to assess semantic coverage.
SIM-CoT: Supervised Implicit Chain-of-Thought
Authors: Xilin Wei; Xiaoran Liu; Yuhang Zang; Xiaoyi Dong; Yuhang Cao; Jiaqi Wang; Xipeng Qiu; Dahua Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Implicit Chain-of-Thought (CoT) methods offer a token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited their adoption.
We identify a core latent instability issue when scaling the computational budget of implicit CoT: as the number of reasoning tokens increases, training often becomes unstable and collapses.
Our analysis shows that this instability arises from latent representations becoming homogeneous and losing semantic diversity, caused by insufficient step-level supervision in current implicit CoT methods.
To address this, we propose SIM-CoT, a plug-and-play training module that introduces step-level supervision to stabilize and enrich the latent reasoning space.
SIM-CoT employs an auxiliary decoder during training to align each implicit token with its corresponding explicit reasoning step, ensuring latent states capture distinct and meaningful information.
The auxiliary decoder is removed at inference, preserving the efficiency of implicit CoT with no added overhead.
It also provides interpretability by projecting each latent token onto an explicit reasoning vocabulary, enabling per-step visualization and diagnosis.
SIM-CoT significantly improves both in-domain accuracy and out-of-domain stability of implicit CoT methods, boosting Coconut by +8.2\% on GPT-2 and CODI by +3.0\% on LLaMA-3.1 8B.
It further surpasses the explicit CoT baseline on GPT-2 by 2.1\% with 2.3$\times$ greater token efficiency, while closing the performance gap on larger models like LLaMA-3.1 8B.