Orionnnn/hub-stats huggingface dataset:
Learning Goal-Oriented Language-Guided Navigation with Self-Improving
  Demonstrations at Scale
Authors: name: Songze Li, name: Zun Wang, name: Gengze Zhou, name: Jialu Li, name: Xiangyu Zeng, name: Limin Wang, name: Yu Qiao, name: Qi Wu, name: Mohit Bansal, name: Yi Wang
Published: 2025-09-29 15:15:54

Goal-oriented language-guided navigation requires robust exploration
capabilities for agents to navigate to specified goals in unknown environments
without step-by-step instructions. Existing methods tend to exclusively utilize
shortest-path trajectories, lacking effective exploration priors for training
navigation agents. To address the above challenges, we present SID, a
goal-oriented language-guided navigation learning approach with Self-Improving
Demonstrations. Specifically, SID learns an initial agent on the shortest-path
data sampled from environments and then leverages this agent to generate novel
exploration trajectories. The novel rollouts provide demonstrations with
stronger exploration strategies to train a better agent, which in turn produces
higher-quality agent demonstrations for the next round of training. We show
that this iterative self-improving pipeline readily scales to new environments,
and the resulting demonstrations can be transferred across a variety of
language-guided navigation tasks, elevating the performance ceiling in diverse
goal-oriented navigation tasks. Extensive experiments demonstrate that SID
significantly boosts the exploration capabilities and generalization of
navigation agents. The resulting agent achieves new state-of-the-art
performance on goal-oriented language-guided navigation tasks, including
REVERIE, SOON, notably achieving a 50.9% success rate on the unseen validation
splits of SOON, surpassing the prior leading approaches by a margin of 13.9%.
MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
Authors: name: Chengyao Wang, name: Zhisheng Zhong, name: Bohao Peng, name: Senqiao Yang, name: Yuqi Liu, name: Haokun Gui, name: Bin Xia, name: Jingyao Li, name: Bei Yu, name: Jiaya Jia
Published: 2025-09-29 17:48:28

We present MGM-Omni, a unified Omni LLM for omni-modal understanding and
expressive, long-horizon speech generation. Unlike cascaded pipelines that
isolate speech synthesis, MGM-Omni adopts a "brain-mouth" design with a
dual-track, token-based architecture that cleanly decouples multimodal
reasoning from real-time speech generation. This design enables efficient
cross-modal interaction and low-latency, streaming speech generation. For
understanding, a unified training strategy coupled with a dual audio encoder
design enables long-form audio perception across diverse acoustic conditions.
For generation, a chunk-based parallel decoding scheme narrows the text speech
token-rate gap, accelerating inference and supporting streaming zero-shot voice
cloning with stable timbre over extended durations. Compared to concurrent
work, MGM-Omni achieves these capabilities with markedly data-efficient
training. Extensive experiments demonstrate that MGM-Omni outperforms existing
open source models in preserving timbre identity across extended sequences,
producing natural and context-aware speech, and achieving superior long-form
audio and omnimodal understanding. MGM-Omni establishes an efficient,
end-to-end paradigm for omnimodal understanding and controllable, personalised
long-horizon speech generation.
LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in
  Mechanism via Multi-Step Reasoning
Authors: name: Shenghao Fu, name: Qize Yang, name: Yuan-Ming Li, name: Xihan Wei, name: Xiaohua Xie, name: Wei-Shi Zheng
Published: 2025-09-29 13:43:55

Long video understanding is still challenging for recent Large Video-Language
Models (LVLMs) due to the conflict between long-form temporal understanding and
detailed spatial perception. LVLMs with a uniform frame sampling mechanism,
which samples frames with an equal frame size and fixed sampling rate,
inevitably sacrifice either temporal clues or spatial details, resulting in
suboptimal solutions. To mitigate this dilemma, we propose LOVE-R1, a model
that can adaptively zoom in on a video clip. The model is first provided with
densely sampled frames but in a small resolution. If some spatial details are
needed, the model can zoom in on a clip of interest with a large frame
resolution based on its reasoning until key visual information is obtained. The
whole process is implemented as a multi-step reasoning process. To train the
reasoning ability, we first finetune the model on our collected 38k
high-quality CoT data and enhance it with decoupled reinforcement finetuning.
As outcome rewards can not provide fine-grained process supervision, we
decouple multi-step reasoning into multiple single-step reasoning and optimize
the internal zoom-in ability explicitly. Experiments on long video
understanding benchmarks show that our model with the slow-fast adaptive frame
sampling mechanism achieves a great trade-off between sampling density and
frame resolutions, and LOVE-R1 outperforms our baseline Qwen2.5-VL by an
average of 3.1% points across 4 common long video understanding benchmarks.
SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable
  Sparse-Linear Attention
Authors: name: Jintao Zhang, name: Haoxu Wang, name: Kai Jiang, name: Shuo Yang, name: Kaiwen Zheng, name: Haocheng Xi, name: Ziteng Wang, name: Hongzhou Zhu, name: Min Zhao, name: Ion Stoica, name: Joseph E. Gonzalez, name: Jun Zhu, name: Jianfei Chen
Published: 2025-09-28 17:58:59

In Diffusion Transformer (DiT) models, particularly for video generation,
attention latency is a major bottleneck due to the long sequence length and the
quadratic complexity. We find that attention weights can be separated into two
parts: a small fraction of large weights with high rank and the remaining
weights with very low rank. This naturally suggests applying sparse
acceleration to the first part and low-rank acceleration to the second. Based
on this finding, we propose SLA (Sparse-Linear Attention), a trainable
attention method that fuses sparse and linear attention to accelerate diffusion
models. SLA classifies attention weights into critical, marginal, and
negligible categories, applying O(N^2) attention to critical weights, O(N)
attention to marginal weights, and skipping negligible ones. SLA combines these
computations into a single GPU kernel and supports both forward and backward
passes. With only a few fine-tuning steps using SLA, DiT models achieve a 20x
reduction in attention computation, resulting in significant acceleration
without loss of generation quality. Experiments show that SLA reduces attention
computation by 95% without degrading end-to-end generation quality,
outperforming baseline methods. In addition, we implement an efficient GPU
kernel for SLA, which yields a 13.7x speedup in attention computation and a
2.2x end-to-end speedup in video generation on Wan2.1-1.3B.
Multiplayer Nash Preference Optimization
Authors: name: Fang Wu, name: Xu Huang, name: Weihao Xuan, name: Zhiwei Zhang, name: Yijia Xiao, name: Guancheng Wan, name: Xiaomin Li, name: Bing Hu, name: Peng Xia, name: Jure Leskovec, name: Yejin Choi
Published: 2025-09-27 04:18:33

Reinforcement learning from human feedback (RLHF) has emerged as the standard
paradigm for aligning large language models (LLMs) with human preferences.
However, reward-based methods built on the Bradley-Terry assumption struggle to
capture the non-transitive and heterogeneous nature of real-world preferences.
To address this, recent studies have reframed alignment as a two-player Nash
game, giving rise to Nash learning from human feedback (NLHF). While this
perspective has inspired algorithms such as INPO, ONPO, and EGPO with strong
theoretical and empirical guarantees, they remain fundamentally restricted to
two-player interactions, creating a single-opponent bias that fails to capture
the full complexity of realistic preference structures. In this work, we
introduce Multiplayer Nash Preference Optimization (MNPO), a novel framework
that generalizes NLHF to the multiplayer regime. It formulates alignment as an
n-player game, where each policy competes against a population of opponents
while being regularized toward a reference model. Our framework establishes
well-defined Nash equilibria in multiplayer settings and extends the concept of
duality gap to quantify approximation quality. We demonstrate that MNPO
inherits the equilibrium guarantees of two-player methods while enabling richer
competitive dynamics and improved coverage of diverse preference structures.
Through comprehensive empirical evaluation, we show that MNPO consistently
outperforms existing NLHF baselines on instruction-following benchmarks,
achieving superior alignment quality under heterogeneous annotator conditions
and mixed-policy evaluation scenarios. Together, these results establish MNPO
as a principled and scalable framework for aligning LLMs with complex,
non-transitive human preferences. Code is available at
https://github.com/smiles724/MNPO.
Democratizing AI scientists using ToolUniverse
Authors: name: Shanghua Gao, name: Richard Zhu, name: Pengwei Sui, name: Zhenglun Kong, name: Sufian Aldogom, name: Yepeng Huang, name: Ayush Noori, name: Reza Shamji, name: Krishna Parvataneni, name: Theodoros Tsiligkaridis, name: Marinka Zitnik
Published: 2025-09-27 17:38:53

AI scientists are emerging computational systems that serve as collaborative
partners in discovery. These systems remain difficult to build because they are
bespoke, tied to rigid workflows, and lack shared environments that unify
tools, data, and analyses into a common ecosystem. In omics, unified ecosystems
have transformed research by enabling interoperability, reuse, and
community-driven development; AI scientists require comparable infrastructure.
We present ToolUniverse, an ecosystem for building AI scientists from any
language or reasoning model, whether open or closed. TOOLUNIVERSE standardizes
how AI scientists identify and call tools, integrating more than 600 machine
learning models, datasets, APIs, and scientific packages for data analysis,
knowledge retrieval, and experimental design. It automatically refines tool
interfaces for correct use by AI scientists, creates new tools from natural
language descriptions, iteratively optimizes tool specifications, and composes
tools into agentic workflows. In a case study of hypercholesterolemia,
ToolUniverse was used to create an AI scientist to identify a potent analog of
a drug with favorable predicted properties. The open-source ToolUniverse is
available at https://aiscientist.tools.
Visual Jigsaw Post-Training Improves MLLMs
Authors: name: Penghao Wu, name: Yushan Zhang, name: Haiwen Diao, name: Bo Li, name: Lewei Lu, name: Ziwei Liu
Published: 2025-09-29 17:59:57

Reinforcement learning based post-training has recently emerged as a powerful
paradigm for enhancing the alignment and reasoning capabilities of multimodal
large language models (MLLMs). While vision-centric post-training is crucial
for enhancing MLLMs' intrinsic understanding of visual signals, current
post-training paradigms are predominantly text-centric, where dense visual
inputs are only leveraged to extract sparse cues for text-based reasoning.
There exist a few approaches in this direction, however, they often still rely
on text as an intermediate mediator or introduce additional visual generative
designs. In this work, we introduce Visual Jigsaw, a generic self-supervised
post-training framework designed to strengthen visual understanding in MLLMs.
Visual Jigsaw is formulated as a general ordering task: visual inputs are
partitioned, shuffled, and the model must reconstruct the visual information by
producing the correct permutation in natural language. This naturally aligns
with reinforcement learning from verifiable rewards (RLVR), requires no
additional visual generative components, and derives its supervisory signal
automatically without any annotations. We instantiate Visual Jigsaw across
three visual modalities, including images, videos, and 3D data. Extensive
experiments demonstrate substantial improvements in fine-grained perception,
temporal reasoning, and 3D spatial understanding. Our findings highlight the
potential of self-supervised vision-centric tasks in post-training MLLMs and
aim to inspire further research on vision-centric pretext designs. Project
Page: https://penghao-wu.github.io/visual_jigsaw/
AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced
  Self-Play
Authors: name: Ran Xu, name: Yuchen Zhuang, name: Zihan Dong, name: Jonathan Wang, name: Yue Yu, name: Joyce C. Ho, name: Linjun Zhang, name: Haoyu Wang, name: Wenqi Shi, name: Carl Yang
Published: 2025-09-29 02:14:30

Search-augmented LLMs often struggle with complex reasoning tasks due to
ineffective multi-hop retrieval and limited reasoning ability. We propose
AceSearcher, a cooperative self-play framework that trains a single large
language model (LLM) to alternate between two roles: a decomposer that breaks
down complex queries and a solver that integrates retrieved contexts for answer
generation. AceSearcher couples supervised fine-tuning on a diverse mixture of
search, reasoning, and decomposition tasks with reinforcement fine-tuning
optimized for final answer accuracy, eliminating the need for intermediate
annotations. Extensive experiments on three reasoning-intensive tasks across 10
datasets show that AceSearcher outperforms state-of-the-art baselines,
achieving an average exact match improvement of 7.6%. Remarkably, on
document-level finance reasoning tasks, AceSearcher-32B matches the performance
of the DeepSeek-V3 model using less than 5% of its parameters. Even at smaller
scales (1.5B and 8B), AceSearcher often surpasses existing search-augmented
LLMs with up to 9x more parameters, highlighting its exceptional efficiency and
effectiveness in tackling complex reasoning tasks. Our code will be published
at https://github.com/ritaranx/AceSearcher and
https://huggingface.co/AceSearcher.
SparseD: Sparse Attention for Diffusion Language Models
Authors: name: Zeqing Wang, name: Gongfan Fang, name: Xinyin Ma, name: Xingyi Yang, name: Xinchao Wang
Published: 2025-09-28 18:10:10

While diffusion language models (DLMs) offer a promising alternative to
autoregressive models (ARs), existing open-source DLMs suffer from high
inference latency. This bottleneck is mainly due to the attention's quadratic
complexity with respect to context length in computing all query-key pairs.
Intuitively, to reduce this complexity, a natural strategy is to restrict
attention to sparse patterns that retain only the most relevant connections.
Such approaches are well-established in ARs, where attention follows fixed and
clearly defined sparse patterns. However, in DLMs, we observe distinct sparsity
behaviors: (1) attention patterns vary across heads, (2) attention patterns in
each head remain highly similar across denoising steps, and (3) early denoising
steps are critical for generation. These findings render sparse attention
methods designed for ARs largely incompatible with DLMs, as they fail to
capture head-specific structures and risk degrading generation when applied in
early denoising steps. To address these challenges, we propose SparseD, a novel
sparse attention method for DLMs. Leveraging the observations, SparseD only
requires pre-computing head-specific sparse patterns one time, and reuses them
across all steps. This prevents recomputing sparse patterns at each denoising
step. Meanwhile, SparseD uses full attention in the early steps, then switches
to sparse attention later to maintain generation quality. Together, these
establish SparseD as a practical and efficient solution for deploying DLMs in
long-context applications. Experimental results demonstrate that SparseD
achieves lossless acceleration, delivering up to 1.50times speedup over
FlashAttention at a 64k context length with 1,024 denoising steps.
EditScore: Unlocking Online RL for Image Editing via High-Fidelity
  Reward Modeling
Authors: name: Xin Luo, name: Jiahao Wang, name: Chenyuan Wu, name: Shitao Xiao, name: Xiyan Jiang, name: Defu Lian, name: Jiajun Zhang, name: Dong Liu, name: Zheng liu
Published: 2025-09-28 14:28:24

Instruction-guided image editing has achieved remarkable progress, yet
current models still face challenges with complex instructions and often
require multiple samples to produce a desired result. Reinforcement Learning
(RL) offers a promising solution, but its adoption in image editing has been
severely hindered by the lack of a high-fidelity, efficient reward signal. In
this work, we present a comprehensive methodology to overcome this barrier,
centered on the development of a state-of-the-art, specialized reward model. We
first introduce EditReward-Bench, a comprehensive benchmark to systematically
evaluate reward models on editing quality. Building on this benchmark, we
develop EditScore, a series of reward models (7B-72B) for evaluating the
quality of instruction-guided image editing. Through meticulous data curation
and filtering, EditScore effectively matches the performance of learning
proprietary VLMs. Furthermore, coupled with an effective self-ensemble strategy
tailored for the generative nature of EditScore, our largest variant even
surpasses GPT-5 in the benchmark. We then demonstrate that a high-fidelity
reward model is the key to unlocking online RL for image editing. Our
experiments show that, while even the largest open-source VLMs fail to provide
an effective learning signal, EditScore enables efficient and robust policy
optimization. Applying our framework to a strong base model, OmniGen2, results
in a final model that shows a substantial and consistent performance uplift.
Overall, this work provides the first systematic path from benchmarking to
reward modeling to RL training in image editing, showing that a high-fidelity,
domain-specialized reward model is the key to unlocking the full potential of
RL in this domain.
Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference
  Learning
Authors: name: Yifei Chen, name: Guanting Dong, name: Zhicheng Dou
Published: 2025-09-27 12:53:37

Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to
improve their internal reasoning ability by integrating external tools.
However, models employing TIR often display suboptimal behaviors, such as
insufficient or excessive tool usage and overthinking after tool calls. The
challenge of incentivizing LLMs to perform TIR efficiently and accurately,
while stabilizing the reasoning process, remains an open question. In this
paper, we start by exploring the impact of tool calls on model reasoning from
the perspective of information entropy. Our findings indicate that tool call
results lead to a distinct change in the information entropy of subsequent
reasoning, with the overall entropy of the reasoning chain varying based on the
number of tool calls. Building on these insights, we propose Tool-Light, a
framework designed to encourage LLMs to perform TIR efficiently and accurately.
Our framework includes dataset construction and multi-stage fine-tuning. For
dataset construction, we employ continuous self-evolved sampling using the
fine-tuned model, integrating both vanilla sampling and entropy-guided
sampling. Besides, we establish strict criteria for selecting positive-negative
pairs during sampling. The training process involves a two-stage approach,
comprising Supervised Fine-Tuning (SFT) and Self-Evolved Direct Preference
Optimization (DPO). Experimental results on 10 datasets demonstrate the
effectiveness of Tool-Light, significantly improving the model's efficiency in
executing TIR tasks.
MultiCrafter: High-Fidelity Multi-Subject Generation via Spatially
  Disentangled Attention and Identity-Aware Reinforcement Learning
Authors: name: Tao Wu, name: Yibo Jiang, name: Yehao Lu, name: Zhizhong Wang, name: Zeyi Huang, name: Zequn Qin, name: Xi Li
Published: 2025-09-26 06:41:43

Multi-subject image generation aims to synthesize user-provided subjects in a
single image while preserving subject fidelity, ensuring prompt consistency,
and aligning with human aesthetic preferences. However, existing methods,
particularly those built on the In-Context-Learning paradigm, are limited by
their reliance on simple reconstruction-based objectives, leading to both
severe attribute leakage that compromises subject fidelity and failing to align
with nuanced human preferences. To address this, we propose MultiCrafter, a
framework that ensures high-fidelity, preference-aligned generation. First, we
find that the root cause of attribute leakage is a significant entanglement of
attention between different subjects during the generation process. Therefore,
we introduce explicit positional supervision to explicitly separate attention
regions for each subject, effectively mitigating attribute leakage. To enable
the model to accurately plan the attention region of different subjects in
diverse scenarios, we employ a Mixture-of-Experts architecture to enhance the
model's capacity, allowing different experts to focus on different scenarios.
Finally, we design a novel online reinforcement learning framework to align the
model with human preferences, featuring a scoring mechanism to accurately
assess multi-subject fidelity and a more stable training strategy tailored for
the MoE architecture. Experiments validate that our framework significantly
improves subject fidelity while aligning with human preferences better.
Alignment through Meta-Weighted Online Sampling: Bridging the Gap
  between Data Generation and Preference Optimization
Authors: name: Junming Yang, name: Ning Xu, name: Biao Liu, name: Shiqi Qiao, name: Xin Geng
Published: 2025-09-27 15:38:24

Preference optimization is crucial for aligning large language models (LLMs)
with human values and intentions. A significant challenge in this process is
the distribution mismatch between pre-collected offline preference data and the
evolving model policy. Existing methods attempt to reduce this gap using static
heuristics or decoupled online sampling strategies, but they often fail to
adapt to the model's dynamic learning state. To bridge this gap, we propose
Meta-Weighted Adaptive Preference Optimization (MetaAPO), a novel framework
that dynamically couples data generation with model training. MetaAPO employs a
lightweight meta-learner, as an "alignment gap estimator", to evaluate the
potential benefits of on-policy sampling in relation to offline data. This
guides targeted online generation and assigns sample-wise meta-weights to the
optimization objective, dynamically balancing the quality and distribution of
online and offline data. Experiments on AlpacaEval 2, Arena-Hard and MT-Bench
demonstrate that MetaAPO consistently outperforms existing preference
optimization approaches across various settings, while reducing 42% in online
annotation costs.