Orionnnn/hub-stats huggingface dataset:
Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time
  Markers
Authors: name: Daniel D'souza, name: Julia Kreutzer, name: Adrien Morisot, name: Ahmet Üstün, name: Sara Hooker
Published: 2025-06-17 16:40:42

One of the most profound challenges of modern machine learning is performing
well on the long-tail of rare and underrepresented features. Large
general-purpose models are trained for many tasks, but work best on
high-frequency use cases. After training, it is hard to adapt a model to
perform well on specific use cases underrepresented in the training corpus.
Relying on prompt engineering or few-shot examples to maximize the output
quality on a particular test case can be frustrating, as models can be highly
sensitive to small changes, react in unpredicted ways or rely on a fixed system
prompt for maintaining performance. In this work, we ask: "Can we optimize our
training protocols to both improve controllability and performance on
underrepresented use cases at inference time?" We revisit the divide between
training and inference techniques to improve long-tail performance while
providing users with a set of control levers the model is trained to be
responsive to. We create a detailed taxonomy of data characteristics and task
provenance to explicitly control generation attributes and implicitly condition
generations at inference time. We fine-tune a base model to infer these markers
automatically, which makes them optional at inference time. This principled and
flexible approach yields pronounced improvements in performance, especially on
examples from the long tail of the training distribution. While we observe an
average lift of 5.7% win rates in open-ended generation quality with our
markers, we see over 9.1% gains in underrepresented domains. We also observe
relative lifts of up to 14.1% on underrepresented tasks like CodeRepair and
absolute improvements of 35.3% on length instruction following evaluations.
Ambient Diffusion Omni: Training Good Models with Bad Data
Authors: name: Giannis Daras, name: Adrian Rodriguez-Munoz, name: Adam Klivans, name: Antonio Torralba, name: Constantinos Daskalakis
Published: 2025-06-10 22:37:39

We show how to use low-quality, synthetic, and out-of-distribution images to
improve the quality of a diffusion model. Typically, diffusion models are
trained on curated datasets that emerge from highly filtered data pools from
the Web and other sources. We show that there is immense value in the
lower-quality images that are often discarded. We present Ambient Diffusion
Omni, a simple, principled framework to train diffusion models that can extract
signal from all available images during training. Our framework exploits two
properties of natural images -- spectral power law decay and locality. We first
validate our framework by successfully training diffusion models with images
synthetically corrupted by Gaussian blur, JPEG compression, and motion blur. We
then use our framework to achieve state-of-the-art ImageNet FID, and we show
significant improvements in both image quality and diversity for text-to-image
generative modeling. The core insight is that noise dampens the initial skew
between the desired high-quality distribution and the mixed distribution we
actually observe. We provide rigorous theoretical justification for our
approach by analyzing the trade-off between learning from biased data versus
limited unbiased data across diffusion times.
Reasoning with Exploration: An Entropy Perspective
Authors: name: Daixuan Cheng, name: Shaohan Huang, name: Xuekai Zhu, name: Bo Dai, name: Wayne Xin Zhao, name: Zhenliang Zhang, name: Furu Wei
Published: 2025-06-17 17:54:03

Balancing exploration and exploitation is a central goal in reinforcement
learning (RL). Despite recent advances in enhancing language model (LM)
reasoning, most methods lean toward exploitation, and increasingly encounter
performance plateaus. In this work, we revisit entropy -- a signal of
exploration in RL -- and examine its relationship to exploratory reasoning in
LMs. Through empirical analysis, we uncover strong positive correlations
between high-entropy regions and three types of exploratory reasoning actions:
(1) pivotal tokens that determine or connect logical steps, (2) reflective
actions such as self-verification and correction, and (3) rare behaviors
under-explored by the base LMs. Motivated by this, we introduce a minimal
modification to standard RL with only one line of code: augmenting the
advantage function with an entropy-based term. Unlike traditional
maximum-entropy methods which encourage exploration by promoting uncertainty,
we encourage exploration by promoting longer and deeper reasoning chains.
Notably, our method achieves significant gains on the Pass@K metric -- an
upper-bound estimator of LM reasoning capabilities -- even when evaluated with
extremely large K values, pushing the boundaries of LM reasoning.
Efficient Medical VIE via Reinforcement Learning
Authors: name: Lijun Liu, name: Ruiyang Li, name: Zhaocheng Liu, name: Chenglin Zhu, name: Chong Li, name: Jiehan Cheng, name: Qiang Ju, name: Jian Xie
Published: 2025-06-16 11:10:25

Visual Information Extraction (VIE) converts unstructured document images
into structured formats like JSON, critical for medical applications such as
report analysis and online consultations. Traditional methods rely on OCR and
language models, while end-to-end multimodal models offer direct JSON
generation. However, domain-specific schemas and high annotation costs limit
their effectiveness in medical VIE. We base our approach on the Reinforcement
Learning with Verifiable Rewards (RLVR) framework to address these challenges
using only 100 annotated samples. Our approach ensures dataset diversity, a
balanced precision-recall reward mechanism to reduce hallucinations and improve
field coverage, and innovative sampling strategies to enhance reasoning
capabilities. Fine-tuning Qwen2.5-VL-7B with our RLVR method, we achieve
state-of-the-art performance on medical VIE tasks, significantly improving F1,
precision, and recall. While our models excel on tasks similar to medical
datasets, performance drops on dissimilar tasks, highlighting the need for
domain-specific optimization. Case studies further demonstrate the value of
reasoning during training and inference for VIE.
Can LLMs Generate High-Quality Test Cases for Algorithm Problems?
  TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure
Authors: name: Zheyuan Yang, name: Zexi Kuang, name: Xue Xia, name: Yilun Zhao
Published: 2025-06-13 23:56:17

We introduce TestCase-Eval, a new benchmark for systematic evaluation of LLMs
in test-case generation. TestCase-Eval includes 500 algorithm problems and
100,000 human-crafted solutions from the Codeforces platform. It focuses on two
pivotal tasks: (1) Fault Coverage, which measures how well LLM-generated test
sets probe diverse input scenarios and cover a wide range of potential failure
modes. (2) Fault Exposure, which evaluates whether LLMs can craft a tailored
test input that reveals a specific incorrect code implementation. We provide a
comprehensive assessment of 19 state-of-the-art open-source and proprietary
LLMs on TestCase-Eval, offering insights into their strengths and limitations
in generating effective test cases for algorithm problems.
QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
Authors: name: Wanlong Liu, name: Junxiao Xu, name: Fei Yu, name: Yukang Lin, name: Ke Ji, name: Wenyu Chen, name: Yan Xu, name: Yasheng Wang, name: Lifeng Shang, name: Benyou Wang
Published: 2025-06-15 14:21:28

Recent advancements in Long Chain-of-Thought (CoT) reasoning models have
improved performance on complex tasks, but they suffer from overthinking, which
generates redundant reasoning steps, especially for simple questions. This
paper revisits the reasoning patterns of Long and Short CoT models, observing
that the Short CoT patterns offer concise reasoning efficiently, while the Long
CoT patterns excel in challenging scenarios where the Short CoT patterns
struggle. To enable models to leverage both patterns, we propose Question-Free
Fine-Tuning (QFFT), a fine-tuning approach that removes the input question
during training and learns exclusively from Long CoT responses. This approach
enables the model to adaptively employ both reasoning patterns: it prioritizes
the Short CoT patterns and activates the Long CoT patterns only when necessary.
Experiments on various mathematical datasets demonstrate that QFFT reduces
average response length by more than 50\%, while achieving performance
comparable to Supervised Fine-Tuning (SFT). Additionally, QFFT exhibits
superior performance compared to SFT in noisy, out-of-domain, and low-resource
scenarios.
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just
  Like an Olympiad Team
Authors: name: Md Tanzib Hosain, name: Salman Rahman, name: Md Kishor Morol, name: Md Rizwan Parvez
Published: 2025-06-17 06:47:19

Despite impressive progress on complex reasoning, current large language
models (LLMs) typically operate in isolation - treating each problem as an
independent attempt, without accumulating or integrating experiential
knowledge. In contrast, expert problem solvers - such as Olympiad or
programming contest teams - leverage a rich tapestry of experiences: absorbing
mentorship from coaches, developing intuition from past problems, leveraging
knowledge of tool usage and library functionality, adapting strategies based on
the expertise and experiences of peers, continuously refining their reasoning
through trial and error, and learning from other related problems even during
competition. We introduce Xolver, a training-free multi-agent reasoning
framework that equips a black-box LLM with a persistent, evolving memory of
holistic experience. Xolver integrates diverse experience modalities, including
external and self-retrieval, tool use, collaborative interactions, agent-driven
evaluation, and iterative refinement. By learning from relevant strategies,
code fragments, and abstract reasoning patterns at inference time, Xolver
avoids generating solutions from scratch - marking a transition from isolated
inference toward experience-aware language agents. Built on both open-weight
and proprietary models, Xolver consistently outperforms specialized reasoning
agents. Even with lightweight backbones (e.g., QWQ-32B), it often surpasses
advanced models including Qwen3-235B, Gemini 2.5 Pro, o3, and o4-mini-high.
With o3-mini-high, it achieves new best results on GSM8K (98.1%), AIME'24
(94.4%), AIME'25 (93.7%), Math-500 (99.8%), and LiveCodeBench-V5 (91.6%) -
highlighting holistic experience learning as a key step toward generalist
agents capable of expert-level reasoning. Code and data are available at
https://kagnlp.github.io/xolver.github.io/.
EfficientVLA: Training-Free Acceleration and Compression for
  Vision-Language-Action Models
Authors: name: Yantai Yang, name: Yuhao Wang, name: Zichen Wen, name: Luo Zhongwei, name: Chang Zou, name: Zhipeng Zhang, name: Chuan Wen, name: Linfeng Zhang
Published: 2025-06-11 18:34:57

Vision-Language-Action (VLA) models, particularly diffusion-based
architectures, demonstrate transformative potential for embodied intelligence
but are severely hampered by high computational and memory demands stemming
from extensive inherent and inference-time redundancies. While existing
acceleration efforts often target isolated inefficiencies, such piecemeal
solutions typically fail to holistically address the varied computational and
memory bottlenecks across the entire VLA pipeline, thereby limiting practical
deployability. We introduce EfficientVLA, a structured and training-free
inference acceleration framework that systematically eliminates these barriers
by cohesively exploiting multifaceted redundancies. EfficientVLA
synergistically integrates three targeted strategies: (1) pruning of
functionally inconsequential layers from the language module, guided by an
analysis of inter-layer redundancies; (2) optimizing the visual processing
pathway through a task-aware strategy that selects a compact, diverse set of
visual tokens, balancing task-criticality with informational coverage; and (3)
alleviating temporal computational redundancy within the iterative
diffusion-based action head by strategically caching and reusing key
intermediate features. We apply our method to a standard VLA model CogACT,
yielding a 1.93X inference speedup and reduces FLOPs to 28.9%, with only a 0.6%
success rate drop in the SIMPLER benchmark.
VideoMolmo: Spatio-Temporal Grounding Meets Pointing
Authors: name: Ghazi Shazan Ahmad, name: Ahmed Heakl, name: Hanan Gani, name: Abdelrahman Shaker, name: Zhiqiang Shen, name: Ranjay Krishna, name: Fahad Shahbaz Khan, name: Salman Khan
Published: 2025-06-05 17:59:29

Spatio-temporal localization is vital for precise interactions across diverse
domains, from biological research to autonomous navigation and interactive
interfaces. Current video-based approaches, while proficient in tracking, lack
the sophisticated reasoning capabilities of large language models, limiting
their contextual understanding and generalization. We introduce VideoMolmo, a
large multimodal model tailored for fine-grained spatio-temporal pointing
conditioned on textual descriptions. Building upon the Molmo architecture,
VideoMolmo incorporates a temporal module utilizing an attention mechanism to
condition each frame on preceding frames, ensuring temporal consistency.
Additionally, our novel temporal mask fusion pipeline employs SAM2 for
bidirectional point propagation, significantly enhancing coherence across video
sequences. This two-step decomposition, i.e., first using the LLM to generate
precise pointing coordinates, then relying on a sequential mask-fusion module
to produce coherent segmentation, not only simplifies the task for the language
model but also enhances interpretability. Due to the lack of suitable datasets,
we curate a comprehensive dataset comprising 72k video-caption pairs annotated
with 100k object points. To evaluate the generalization of VideoMolmo, we
introduce VPoS-Bench, a challenging out-of-distribution benchmark spanning five
real-world scenarios: Cell Tracking, Egocentric Vision, Autonomous Driving,
Video-GUI Interaction, and Robotics. We also evaluate our model on Referring
Video Object Segmentation (Refer-VOS) and Reasoning VOS tasks. In comparison to
existing models, VideoMolmo substantially improves spatio-temporal pointing
accuracy and reasoning capability. Our code and models are publicly available
at https://github.com/mbzuai-oryx/VideoMolmo.
CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility
  Simulation
Authors: name: Yuwei Du, name: Jie Feng, name: Jian Yuan, name: Yong Li
Published: 2025-06-16 15:24:07

Human mobility simulation plays a crucial role in various real-world
applications. Recently, to address the limitations of traditional data-driven
approaches, researchers have explored leveraging the commonsense knowledge and
reasoning capabilities of large language models (LLMs) to accelerate human
mobility simulation. However, these methods suffer from several critical
shortcomings, including inadequate modeling of urban spaces and poor
integration with both individual mobility patterns and collective mobility
distributions. To address these challenges, we propose CityGPT-Powered
Agentic framework for Mobility Simulation
(CAMS), an agentic framework that leverages the language based urban
foundation model to simulate human mobility in urban space. CAMS
comprises three core modules, including MobExtractor to extract template
mobility patterns and synthesize new ones based on user profiles, GeoGenerator
to generate anchor points considering collective knowledge and generate
candidate urban geospatial knowledge using an enhanced version of CityGPT,
TrajEnhancer to retrieve spatial knowledge based on mobility patterns and
generate trajectories with real trajectory preference alignment via DPO.
Experiments on real-world datasets show that CAMS achieves superior
performance without relying on externally provided geospatial information.
Moreover, by holistically modeling both individual mobility patterns and
collective mobility constraints, CAMS generates more realistic and
plausible trajectories. In general, CAMS establishes a new paradigm
that integrates the agentic framework with urban-knowledgeable LLMs for human
mobility simulation.
Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes
  Correct Reasoning in Base LLMs
Authors: name: Xumeng Wen, name: Zihan Liu, name: Shun Zheng, name: Zhijian Xu, name: Shengyu Ye, name: Zhirong Wu, name: Xiao Liang, name: Yang Wang, name: Junjie Li, name: Ziming Miao, name: Jiang Bian, name: Mao Yang
Published: 2025-06-17 07:06:56

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a
promising paradigm for advancing the reasoning capabilities of Large Language
Models (LLMs). However, a critical paradox clouds its efficacy: RLVR-tuned
models often underperform their base models on the Pass@K metric for
solution-finding, leading to the hypothesis that RLVR merely re-weights
existing reasoning paths at the cost of reasoning diversity. In this work, we
resolve this contradiction by identifying the source of the problem: the
Pass@K metric itself is a flawed measure of reasoning, as it credits correct
final answers that probably arise from inaccurate or incomplete chains of
thought (CoTs). To address this, we introduce a more precise evaluation metric,
CoT-Pass@K, which mandates that both the reasoning path and the final
answer be correct. We provide a new theoretical foundation that formalizes how
RLVR, unlike traditional RL, is uniquely structured to incentivize logical
integrity. Our empirical results are supportive: using CoT-Pass@K, we
observe that RLVR can incentivize the generalization of correct reasoning for
all values of K. Furthermore, by analyzing the training dynamics, we find
that this enhanced reasoning capability emerges early in the training process
and smoothly generalizes. Our work provides a clear perspective on the role of
RLVR, offers a more reliable method for its evaluation, and confirms its
potential to genuinely advance machine reasoning.
Optimizing Length Compression in Large Reasoning Models
Authors: name: Zhengxiang Cheng, name: Dongping Chen, name: Mingyang Fu, name: Tianyi Zhou
Published: 2025-06-17 17:50:16

Large Reasoning Models (LRMs) have achieved remarkable success, yet they
often suffer from producing unnecessary and verbose reasoning chains. We
identify a core aspect of this issue as "invalid thinking" -- models tend to
repeatedly double-check their work after having derived the correct answer. To
address this specific inefficiency, we move beyond the general principles of
Efficacy and Efficiency to propose two new, fine-grained principles: Brevity,
which advocates for eliminating redundancy, and Sufficiency, which ensures
critical reasoning steps are preserved. Guided by these principles, we
introduce LC-R1, a post-training method based on Group Relative Policy
Optimization (GRPO). LC-R1 employs a novel combination of a Length Reward for
overall conciseness and a Compress Reward that is specifically designed to
remove the invalid portion of the thinking process. Extensive experiments on
multiple reasoning benchmarks demonstrate that LC-R1 achieves a significant
reduction in sequence length (~50%) with only a marginal (~2%) drop in
accuracy, achieving a favorable trade-off point on the Pareto frontier that
prioritizes high compression. Our analysis further validates the robustness of
LC-R1 and provides valuable insights for developing more powerful yet
computationally efficient LRMs. Our code is released at
https://github.com/zxiangx/LC-R1.
TR2M: Transferring Monocular Relative Depth to Metric Depth with
  Language Descriptions and Scale-Oriented Contrast
Authors: name: Beilei Cui, name: Yiming Huang, name: Long Bai, name: Hongliang Ren
Published: 2025-06-16 11:50:00

This work presents a generalizable framework to transfer relative depth to
metric depth. Current monocular depth estimation methods are mainly divided
into metric depth estimation (MMDE) and relative depth estimation (MRDE). MMDEs
estimate depth in metric scale but are often limited to a specific domain.
MRDEs generalize well across different domains, but with uncertain scales which
hinders downstream applications. To this end, we aim to build up a framework to
solve scale uncertainty and transfer relative depth to metric depth. Previous
methods used language as input and estimated two factors for conducting
rescaling. Our approach, TR2M, utilizes both text description and image as
inputs and estimates two rescale maps to transfer relative depth to metric
depth at pixel level. Features from two modalities are fused with a
cross-modality attention module to better capture scale information. A strategy
is designed to construct and filter confident pseudo metric depth for more
comprehensive supervision. We also develop scale-oriented contrastive learning
to utilize depth distribution as guidance to enforce the model learning about
intrinsic knowledge aligning with the scale distribution. TR2M only exploits a
small number of trainable parameters to train on datasets in various domains
and experiments not only demonstrate TR2M's great performance in seen datasets
but also reveal superior zero-shot capabilities on five unseen datasets. We
show the huge potential in pixel-wise transferring relative depth to metric
depth with language assistance. (Code is available at:
https://github.com/BeileiCui/TR2M)
Align Your Flow: Scaling Continuous-Time Flow Map Distillation
Authors: name: Amirmojtaba Sabour, name: Sanja Fidler, name: Karsten Kreis
Published: 2025-06-17 15:06:07

Diffusion- and flow-based models have emerged as state-of-the-art generative
modeling approaches, but they require many sampling steps. Consistency models
can distill these models into efficient one-step generators; however, unlike
flow- and diffusion-based methods, their performance inevitably degrades when
increasing the number of steps, which we show both analytically and
empirically. Flow maps generalize these approaches by connecting any two noise
levels in a single step and remain effective across all step counts. In this
paper, we introduce two new continuous-time objectives for training flow maps,
along with additional novel training techniques, generalizing existing
consistency and flow matching objectives. We further demonstrate that
autoguidance can improve performance, using a low-quality model for guidance
during distillation, and an additional boost can be achieved by adversarial
finetuning, with minimal loss in sample diversity. We extensively validate our
flow map models, called Align Your Flow, on challenging image generation
benchmarks and achieve state-of-the-art few-step generation performance on both
ImageNet 64x64 and 512x512, using small and efficient neural networks. Finally,
we show text-to-image flow map models that outperform all existing
non-adversarially trained few-step samplers in text-conditioned synthesis.