Orionnnn/hub-stats huggingface dataset:
A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths
  to Reproducibility
Authors: name: Andreas Hochlehnert, name: Hardik Bhatnagar, name: Vishaal Udandarao, name: Samuel Albanie, name: Ameya Prabhu, name: Matthias Bethge
Published: 2025-04-09 17:58:17

Reasoning has emerged as the next major frontier for language models (LMs),
with rapid advances from both academic and industrial labs. However, this
progress often outpaces methodological rigor, with many evaluations relying on
benchmarking practices that lack transparency, robustness, or statistical
grounding. In this work, we conduct a comprehensive empirical study and find
that current mathematical reasoning benchmarks are highly sensitive to subtle
implementation choices - including decoding parameters, random seeds, prompt
formatting, and even hardware and software-framework configurations.
Performance gains reported in recent studies frequently hinge on unclear
comparisons or unreported sources of variance. To address these issues, we
propose a standardized evaluation framework with clearly defined best practices
and reporting standards. Using this framework, we reassess recent methods and
find that reinforcement learning (RL) approaches yield only modest improvements
- far below prior claims - and are prone to overfitting, especially on
small-scale benchmarks like AIME24. In contrast, supervised finetuning (SFT)
methods show consistently stronger generalization. To foster reproducibility,
we release all code, prompts, and model outputs, for reasoning benchmarks,
establishing more rigorous foundations for future work.
GenDoP: Auto-regressive Camera Trajectory Generation as a Director of
  Photography
Authors: name: Mengchen Zhang, name: Tong Wu, name: Jing Tan, name: Ziwei Liu, name: Gordon Wetzstein, name: Dahua Lin
Published: 2025-04-09 17:56:01

Camera trajectory design plays a crucial role in video production, serving as
a fundamental tool for conveying directorial intent and enhancing visual
storytelling. In cinematography, Directors of Photography meticulously craft
camera movements to achieve expressive and intentional framing. However,
existing methods for camera trajectory generation remain limited: Traditional
approaches rely on geometric optimization or handcrafted procedural systems,
while recent learning-based methods often inherit structural biases or lack
textual alignment, constraining creative synthesis. In this work, we introduce
an auto-regressive model inspired by the expertise of Directors of Photography
to generate artistic and expressive camera trajectories. We first introduce
DataDoP, a large-scale multi-modal dataset containing 29K real-world shots with
free-moving camera trajectories, depth maps, and detailed captions in specific
movements, interaction with the scene, and directorial intent. Thanks to the
comprehensive and diverse database, we further train an auto-regressive,
decoder-only Transformer for high-quality, context-aware camera movement
generation based on text guidance and RGBD inputs, named GenDoP. Extensive
experiments demonstrate that compared to existing methods, GenDoP offers better
controllability, finer-grained trajectory adjustments, and higher motion
stability. We believe our approach establishes a new standard for
learning-based cinematography, paving the way for future advancements in camera
control and filmmaking. Our project website:
https://kszpxxzmc.github.io/GenDoP/.
Missing Premise exacerbates Overthinking: Are Reasoning Models losing
  Critical Thinking Skill?
Authors: name: Chenrui Fan, name: Ming Li, name: Lichao Sun, name: Tianyi Zhou
Published: 2025-04-09 01:25:27

We find that the response length of reasoning LLMs, whether trained by
reinforcement learning or supervised learning, drastically increases for
ill-posed questions with missing premises (MiP), ending up with redundant and
ineffective thinking. This newly introduced scenario exacerbates the general
overthinking issue to a large extent, which we name as the MiP-Overthinking.
Such failures are against the ``test-time scaling law'' but have been widely
observed on multiple datasets we curated with MiP, indicating the harm of cheap
overthinking and a lack of critical thinking. Surprisingly, LLMs not
specifically trained for reasoning exhibit much better performance on the MiP
scenario, producing much shorter responses that quickly identify ill-posed
queries. This implies a critical flaw of the current training recipe for
reasoning LLMs, which does not encourage efficient thinking adequately, leading
to the abuse of thinking patterns. To further investigate the reasons behind
such failures, we conduct fine-grained analyses of the reasoning length,
overthinking patterns, and location of critical thinking on different types of
LLMs. Moreover, our extended ablation study reveals that the overthinking is
contagious through the distillation of reasoning models' responses. These
results improve the understanding of overthinking and shed novel insights into
mitigating the problem.
FantasyTalking: Realistic Talking Portrait Generation via Coherent
  Motion Synthesis
Authors: name: Mengchao Wang, name: Qiang Wang, name: Fan Jiang, name: Yaqi Fan, name: Yunpeng Zhang, name: Yonggang Qi, name: Kun Zhao, name: Mu Xu
Published: 2025-04-07 08:56:01

Creating a realistic animatable avatar from a single static portrait remains
challenging. Existing approaches often struggle to capture subtle facial
expressions, the associated global body movements, and the dynamic background.
To address these limitations, we propose a novel framework that leverages a
pretrained video diffusion transformer model to generate high-fidelity,
coherent talking portraits with controllable motion dynamics. At the core of
our work is a dual-stage audio-visual alignment strategy. In the first stage,
we employ a clip-level training scheme to establish coherent global motion by
aligning audio-driven dynamics across the entire scene, including the reference
portrait, contextual objects, and background. In the second stage, we refine
lip movements at the frame level using a lip-tracing mask, ensuring precise
synchronization with audio signals. To preserve identity without compromising
motion flexibility, we replace the commonly used reference network with a
facial-focused cross-attention module that effectively maintains facial
consistency throughout the video. Furthermore, we integrate a motion intensity
modulation module that explicitly controls expression and body motion
intensity, enabling controllable manipulation of portrait movements beyond mere
lip motion. Extensive experimental results show that our proposed approach
achieves higher quality with better realism, coherence, motion intensity, and
identity preservation. Ours project page:
https://fantasy-amap.github.io/fantasy-talking/.
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training
  Tokens
Authors: name: Jiacheng Liu, name: Taylor Blanton, name: Yanai Elazar, name: Sewon Min, name: YenSung Chen, name: Arnavi Chheda-Kothary, name: Huy Tran, name: Byron Bischoff, name: Eric Marsh, name: Michael Schmitz, name: Cassidy Trier, name: Aaron Sarnat, name: Jenna James, name: Jon Borchardt, name: Bailey Kuehl, name: Evie Cheng, name: Karen Farley, name: Sruthi Sreeram, name: Taira Anderson, name: David Albright, name: Carissa Schoenick, name: Luca Soldaini, name: Dirk Groeneveld, name: Rock Yuren Pang, name: Pang Wei Koh, name: Noah A. Smith, name: Sophie Lebrecht, name: Yejin Choi, name: Hannaneh Hajishirzi, name: Ali Farhadi, name: Jesse Dodge
Published: 2025-04-09 17:59:35

We present OLMoTrace, the first system that traces the outputs of language
models back to their full, multi-trillion-token training data in real time.
OLMoTrace finds and shows verbatim matches between segments of language model
output and documents in the training text corpora. Powered by an extended
version of infini-gram (Liu et al., 2024), our system returns tracing results
within a few seconds. OLMoTrace can help users understand the behavior of
language models through the lens of their training data. We showcase how it can
be used to explore fact checking, hallucination, and the creativity of language
models. OLMoTrace is publicly available and fully open-source.
DDT: Decoupled Diffusion Transformer
Authors: name: Shuai Wang, name: Zhi Tian, name: Weilin Huang, name: Limin Wang
Published: 2025-04-08 07:17:45

Diffusion transformers have demonstrated remarkable generation quality,
albeit requiring longer training iterations and numerous inference steps. In
each denoising step, diffusion transformers encode the noisy inputs to extract
the lower-frequency semantic component and then decode the higher frequency
with identical modules. This scheme creates an inherent optimization dilemma:
encoding low-frequency semantics necessitates reducing high-frequency
components, creating tension between semantic encoding and high-frequency
decoding. To resolve this challenge, we propose a new
\color{ddtD}ecoupled \color{ddtD}iffusion
\color{ddtT}ransformer~(\color{ddtDDT}), with a decoupled
design of a dedicated condition encoder for semantic extraction alongside a
specialized velocity decoder. Our experiments reveal that a more substantial
encoder yields performance improvements as model size increases. For ImageNet
256times256, Our DDT-XL/2 achieves a new state-of-the-art performance of
{1.31 FID}~(nearly 4times faster training convergence compared to previous
diffusion transformers). For ImageNet 512times512, Our DDT-XL/2 achieves a
new state-of-the-art FID of 1.28. Additionally, as a beneficial by-product, our
decoupled architecture enhances inference speed by enabling the sharing
self-condition between adjacent denoising steps. To minimize performance
degradation, we propose a novel statistical dynamic programming approach to
identify optimal sharing strategies.
SkillWeaver: Web Agents can Self-Improve by Discovering and Honing
  Skills
Authors: name: Boyuan Zheng, name: Michael Y. Fatemi, name: Xiaolong Jin, name: Zora Zhiruo Wang, name: Apurva Gandhi, name: Yueqi Song, name: Yu Gu, name: Jayanth Srinivasa, name: Gaowen Liu, name: Graham Neubig, name: Yu Su
Published: 2025-04-09 17:51:50

To survive and thrive in complex environments, humans have evolved
sophisticated self-improvement mechanisms through environment exploration,
hierarchical abstraction of experiences into reuseable skills, and
collaborative construction of an ever-growing skill repertoire. Despite recent
advancements, autonomous web agents still lack crucial self-improvement
capabilities, struggling with procedural knowledge abstraction, refining
skills, and skill composition. In this work, we introduce SkillWeaver, a
skill-centric framework enabling agents to self-improve by autonomously
synthesizing reusable skills as APIs. Given a new website, the agent
autonomously discovers skills, executes them for practice, and distills
practice experiences into robust APIs. Iterative exploration continually
expands a library of lightweight, plug-and-play APIs, significantly enhancing
the agent's capabilities. Experiments on WebArena and real-world websites
demonstrate the efficacy of SkillWeaver, achieving relative success rate
improvements of 31.8% and 39.8%, respectively. Additionally, APIs synthesized
by strong agents substantially enhance weaker agents through transferable
skills, yielding improvements of up to 54.3% on WebArena. These results
demonstrate the effectiveness of honing diverse website interactions into APIs,
which can be seamlessly shared among various web agents.
Unified World Models: Coupling Video and Action Diffusion for
  Pretraining on Large Robotic Datasets
Authors: name: Chuning Zhu, name: Raymond Yu, name: Siyuan Feng, name: Benjamin Burchfiel, name: Paarth Shah, name: Abhishek Gupta
Published: 2025-04-03 17:38:59

Imitation learning has emerged as a promising approach towards building
generalist robots. However, scaling imitation learning for large robot
foundation models remains challenging due to its reliance on high-quality
expert demonstrations. Meanwhile, large amounts of video data depicting a wide
range of environments and diverse behaviors are readily available. This data
provides a rich source of information about real-world dynamics and
agent-environment interactions. Leveraging this data directly for imitation
learning, however, has proven difficult due to the lack of action annotation
required for most contemporary methods. In this work, we present Unified World
Models (UWM), a framework that allows for leveraging both video and action data
for policy learning. Specifically, a UWM integrates an action diffusion process
and a video diffusion process within a unified transformer architecture, where
independent diffusion timesteps govern each modality. We show that by simply
controlling each diffusion timestep, UWM can flexibly represent a policy, a
forward dynamics, an inverse dynamics, and a video generator. Through simulated
and real-world experiments, we show that: (1) UWM enables effective pretraining
on large-scale multitask robot datasets with both dynamics and action
predictions, resulting in more generalizable and robust policies than imitation
learning, (2) UWM naturally facilitates learning from action-free video data
through independent control of modality-specific diffusion timesteps, further
improving the performance of finetuned policies. Our results suggest that UWM
offers a promising step toward harnessing large, heterogeneous datasets for
scalable robot learning, and provides a simple unification between the often
disparate paradigms of imitation learning and world modeling. Videos and code
are available at https://weirdlabuw.github.io/uwm/.
ProtoGCD: Unified and Unbiased Prototype Learning for Generalized
  Category Discovery
Authors: name: Shijie Ma, name: Fei Zhu, name: Xu-Yao Zhang, name: Cheng-Lin Liu
Published: 2025-04-02 06:13:14

Generalized category discovery (GCD) is a pragmatic but underexplored
problem, which requires models to automatically cluster and discover novel
categories by leveraging the labeled samples from old classes. The challenge is
that unlabeled data contain both old and new classes. Early works leveraging
pseudo-labeling with parametric classifiers handle old and new classes
separately, which brings about imbalanced accuracy between them. Recent methods
employing contrastive learning neglect potential positives and are decoupled
from the clustering objective, leading to biased representations and
sub-optimal results. To address these issues, we introduce a unified and
unbiased prototype learning framework, namely ProtoGCD, wherein old and new
classes are modeled with joint prototypes and unified learning objectives,
{enabling unified modeling between old and new classes}. Specifically, we
propose a dual-level adaptive pseudo-labeling mechanism to mitigate
confirmation bias, together with two regularization terms to collectively help
learn more suitable representations for GCD. Moreover, for practical
considerations, we devise a criterion to estimate the number of new classes.
Furthermore, we extend ProtoGCD to detect unseen outliers, achieving task-level
unification. Comprehensive experiments show that ProtoGCD achieves
state-of-the-art performance on both generic and fine-grained datasets. The
code is available at https://github.com/mashijie1028/ProtoGCD.
HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient
  MoE Inference
Authors: name: Shuzhang Zhong, name: Yanfan Sun, name: Ling Liang, name: Runsheng Wang, name: Ru Huang, name: Meng Li
Published: 2025-04-08 10:47:37

The Mixture of Experts (MoE) architecture has demonstrated significant
advantages as it enables to increase the model capacity without a proportional
increase in computation. However, the large MoE model size still introduces
substantial memory demands, which usually requires expert offloading on
resource-constrained platforms and incurs significant overhead. Hybrid CPU-GPU
inference has been proposed to leverage CPU computation to reduce expert
loading overhead but faces major challenges: on one hand, the expert activation
patterns of MoE models are highly unstable, rendering the fixed mapping
strategies in existing works inefficient; on the other hand, the hybrid CPU-GPU
schedule for MoE is inherently complex due to the diverse expert sizes,
structures, uneven workload distribution, etc. To address these challenges, in
this paper, we propose HybriMoE, a hybrid CPU-GPU inference framework that
improves resource utilization through a novel CPU-GPU scheduling and cache
management system. HybriMoE introduces (i) a dynamic intra-layer scheduling
strategy to balance workloads across CPU and GPU, (ii) an impact-driven
inter-layer prefetching algorithm, and (iii) a score-based caching algorithm to
mitigate expert activation instability. We implement HybriMoE on top of the
kTransformers framework and evaluate it on three widely used MoE-based LLMs.
Experimental results demonstrate that HybriMoE achieves an average speedup of
1.33times in the prefill stage and 1.70times in the decode stage compared
to state-of-the-art hybrid MoE inference framework. Our code is available at:
https://github.com/PKU-SEC-Lab/HybriMoE.
Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
Authors: name: Taiwei Shi, name: Yiyang Wu, name: Linxin Song, name: Tianyi Zhou, name: Jieyu Zhao
Published: 2025-04-07 21:31:31

Reinforcement finetuning (RFT) has shown great potential for enhancing the
mathematical reasoning capabilities of large language models (LLMs), but it is
often sample- and compute-inefficient, requiring extensive training. In this
work, we introduce AdaRFT (Adaptive Curriculum Reinforcement Finetuning), a
method that significantly improves both the efficiency and final accuracy of
RFT through adaptive curriculum learning. AdaRFT dynamically adjusts the
difficulty of training problems based on the model's recent reward signals,
ensuring that the model consistently trains on tasks that are challenging but
solvable. This adaptive sampling strategy accelerates learning by maintaining
an optimal difficulty range, avoiding wasted computation on problems that are
too easy or too hard. AdaRFT requires only a lightweight extension to standard
RFT algorithms like Proximal Policy Optimization (PPO), without modifying the
reward function or model architecture. Experiments on competition-level math
datasets-including AMC, AIME, and IMO-style problems-demonstrate that AdaRFT
significantly improves both training efficiency and reasoning performance. We
evaluate AdaRFT across multiple data distributions and model sizes, showing
that it reduces the number of training steps by up to 2x and improves accuracy
by a considerable margin, offering a more scalable and effective RFT framework.
Leanabell-Prover: Posttraining Scaling in Formal Reasoning
Authors: name: Jingyuan Zhang, name: Qi Wang, name: Xingguang Ji, name: Yahui Liu, name: Yang Yue, name: Fuzheng Zhang, name: Di Zhang, name: Guorui Zhou, name: Kun Gai
Published: 2025-04-08 15:15:26

Recent advances in automated theorem proving (ATP) through LLMs have
highlighted the potential of formal reasoning with Lean 4 codes. However, ATP
has not yet be revolutionized by the recent posttraining scaling as
demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the
entire posttraining of ATP, aiming to align it with breakthroughs in reasoning
models in natural languages.To begin, we continual train current ATP models
with a hybrid dataset, which consists of numerous statement-proof pairs, and
additional data aimed at incorporating cognitive behaviors that emulate human
reasoning and hypothesis refinement. Next, we explore reinforcement learning
with the use of outcome reward returned by Lean 4 compiler. Through our
designed continual training and reinforcement learning processes, we have
successfully improved existing formal provers, including both
DeepSeek-Prover-v1.5 and Goedel-Prover, achieving state-of-the-art performance
in the field of whole-proof generation. For example, we achieve a 59.8% pass
rate (pass@32) on MiniF2F. This is an on-going project and we will
progressively update our findings, release our data and training details.
HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned
  Guidance
Authors: name: Jiazi Bu, name: Pengyang Ling, name: Yujie Zhou, name: Pan Zhang, name: Tong Wu, name: Xiaoyi Dong, name: Yuhang Zang, name: Yuhang Cao, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-04-08 17:30:40

Text-to-image (T2I) diffusion/flow models have drawn considerable attention
recently due to their remarkable ability to deliver flexible visual creations.
Still, high-resolution image synthesis presents formidable challenges due to
the scarcity and complexity of high-resolution content. To this end, we present
HiFlow, a training-free and model-agnostic framework to unlock the resolution
potential of pre-trained flow models. Specifically, HiFlow establishes a
virtual reference flow within the high-resolution space that effectively
captures the characteristics of low-resolution flow information, offering
guidance for high-resolution generation through three key aspects:
initialization alignment for low-frequency consistency, direction alignment for
structure preservation, and acceleration alignment for detail fidelity. By
leveraging this flow-aligned guidance, HiFlow substantially elevates the
quality of high-resolution image synthesis of T2I models and demonstrates
versatility across their personalized variants. Extensive experiments validate
HiFlow's superiority in achieving superior high-resolution image quality over
current state-of-the-art methods.
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for
  Alignment with Human Values
Authors: name: M-A-P Team, name: Siwei Wu, name: Jincheng Ren, name: Xinrun Du, name: Shuyue Guo, name: Xingwei Qu, name: Yiming Liang, name: Jie Liu, name: Yunwen Li, name: Tianyu Zheng, name: Boyu Feng, name: Huaqing Yuan, name: Zenith Wang, name: Jiaheng Liu, name: Wenhao Huang, name: Chenglin Cai, name: Haoran Que, name: Jian Yang, name: Yuelin Bai, name: Zekun Moore Wang, name: Zhouliang Yu, name: Qunshu Lin, name: Ding Pan, name: Yuchen Jiang, name: Tiannan Wang, name: Wangchunshu Zhou, name: Shenzhi Wang, name: Xingyuan Bu, name: Minghao Liu, name: Guoyin Wang, name: Ge Zhang, name: Chenghua Lin
Published: 2025-04-07 22:15:51

Aligning large language models (LLMs) with human preferences has achieved
remarkable success. However, existing Chinese preference datasets are limited
by small scale, narrow domain coverage, and lack of rigorous data validation.
Additionally, the reliance on human annotators for instruction and response
labeling significantly constrains the scalability of human preference datasets.
To address these challenges, we design an LLM-based Chinese preference dataset
annotation pipeline with no human intervention. Specifically, we crawled and
carefully filtered 92k high-quality Chinese queries and employed 15 mainstream
LLMs to generate and score chosen-rejected response pairs. Based on it, we
introduce COIG-P (Chinese Open Instruction Generalist - Preference), a
high-quality, large-scale Chinese preference dataset, comprises 1,009k Chinese
preference pairs spanning 6 diverse domains: Chat, Code, Math, Logic, Novel,
and Role. Building upon COIG-P, to reduce the overhead of using LLMs for
scoring, we trained a 8B-sized Chinese Reward Model (CRM) and meticulously
constructed a Chinese Reward Benchmark (CRBench). Evaluation results based on
AlignBench liu2024alignbenchbenchmarkingchinesealignment show that that
COIG-P significantly outperforms other Chinese preference datasets, and it
brings significant performance improvements ranging from 2% to 12% for the
Qwen2/2.5 and Infinity-Instruct-3M-0625 model series, respectively. The results
on CRBench demonstrate that our CRM has a strong and robust scoring ability. We
apply it to filter chosen-rejected response pairs in a test split of COIG-P,
and our experiments show that it is comparable to GPT-4o in identifying
low-quality samples while maintaining efficiency and cost-effectiveness. Our
codes and data are released in
https://github.com/multimodal-art-projection/COIG-P.