Orionnnn/hub-stats huggingface dataset:
Reasoning Language Models: A Blueprint
Authors: name: Maciej Besta, name: Julia Barth, name: Eric Schreiber, name: Ales Kubicek, name: Afonso Catarino, name: Robert Gerstenberger, name: Piotr Nyczyk, name: Patrick Iff, name: Yueling Li, name: Sam Houliston, name: Tomasz Sternal, name: Marcin Copik, name: Grzegorz Kwaśniewski, name: Jürgen Müller, name: Łukasz Flis, name: Hannes Eberhard, name: Hubert Niewiadomski, name: Torsten Hoefler
Published: 2025-01-20 02:16:19

Reasoning language models (RLMs), also known as Large Reasoning Models
(LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have
redefined AI's problem-solving capabilities by extending large language models
(LLMs) with advanced reasoning mechanisms. Yet, their high costs, proprietary
nature, and complex architectures - uniquely combining Reinforcement Learning
(RL), search heuristics, and LLMs - present accessibility and scalability
challenges. To address these, we propose a comprehensive blueprint that
organizes RLM components into a modular framework, based on a survey and
analysis of all RLM works. This blueprint incorporates diverse reasoning
structures (chains, trees, graphs, and nested forms), reasoning strategies
(e.g., Monte Carlo Tree Search, Beam Search), RL concepts (policy, value models
and others), and supervision schemes (Output-Based and Process-Based
Supervision). We also provide detailed mathematical formulations and
algorithmic specifications to simplify RLM implementation. By showing how
schemes like LLaMA-Berry, QwQ, Journey Learning, and Graph of Thoughts fit as
special cases, we demonstrate the blueprint's versatility and unifying
potential. To illustrate its utility, we introduce x1, a modular implementation
for rapid RLM prototyping and experimentation. Using x1 and a literature
review, we provide key insights, such as multi-phase training for policy and
value models, and the importance of familiar training distributions. Finally,
we outline how RLMs can integrate with a broader LLM ecosystem, including tools
and databases. Our work demystifies RLM construction, democratizes advanced
reasoning capabilities, and fosters innovation, aiming to mitigate the gap
between "rich AI" and "poor AI" by lowering barriers to RLM development and
experimentation.
GPS as a Control Signal for Image Generation
Authors: name: Chao Feng, name: Ziyang Chen, name: Aleksander Holynski, name: Alexei A. Efros, name: Andrew Owens
Published: 2025-01-21 18:59:46

We show that the GPS tags contained in photo metadata provide a useful
control signal for image generation. We train GPS-to-image models and use them
for tasks that require a fine-grained understanding of how images vary within a
city. In particular, we train a diffusion model to generate images conditioned
on both GPS and text. The learned model generates images that capture the
distinctive appearance of different neighborhoods, parks, and landmarks. We
also extract 3D models from 2D GPS-to-image models through score distillation
sampling, using GPS conditioning to constrain the appearance of the
reconstruction from each viewpoint. Our evaluations suggest that our
GPS-conditioned models successfully learn to generate images that vary based on
location, and that GPS conditioning improves estimated 3D structure.
Demons in the Detail: On Implementing Load Balancing Loss for Training
  Specialized Mixture-of-Expert Models
Authors: name: Zihan Qiu, name: Zeyu Huang, name: Bo Zheng, name: Kaiyue Wen, name: Zekun Wang, name: Rui Men, name: Ivan Titov, name: Dayiheng Liu, name: Jingren Zhou, name: Junyang Lin
Published: 2025-01-21 04:04:39

This paper revisits the implementation of
Load-balancing Loss (LBL) when training
Mixture-of-Experts (MoEs) models. Specifically, LBL for MoEs is defined as N_E
sum_{i=1}^{N_E} f_i p_i, where N_E is the total number of experts, f_i
represents the frequency of expert i being selected, and p_i denotes the
average gating score of the expert i. Existing MoE training frameworks
usually employ the parallel training strategy so that f_i and the LBL are
calculated within a micro-batch and then averaged across parallel
groups. In essence, a micro-batch for training billion-scale LLMs normally
contains very few sequences. So, the micro-batch LBL is almost at the sequence
level, and the router is pushed to distribute the token evenly within each
sequence. Under this strict constraint, even tokens from a domain-specific
sequence (e.g., code) are uniformly routed to all experts, thereby
inhibiting expert specialization. In this work, we propose calculating LBL
using a global-batch to loose this constraint. Because a
global-batch contains much more diverse sequences than a micro-batch, which
will encourage load balance at the corpus level. Specifically, we introduce an
extra communication step to synchronize f_i across micro-batches and then use
it to calculate the LBL. Through experiments on training MoEs-based LLMs (up to
42.8B total parameters and 400B tokens), we surprisingly
find that the global-batch LBL strategy yields excellent performance gains in
both pre-training perplexity and downstream tasks. Our analysis reveals that
the global-batch LBL also greatly improves the domain specialization of MoE
experts.
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Authors: name: Yilun Zhao, name: Lujing Xie, name: Haowei Zhang, name: Guo Gan, name: Yitao Long, name: Zhiyuan Hu, name: Tongyan Hu, name: Weiyuan Chen, name: Chuhan Li, name: Junyang Song, name: Zhijian Xu, name: Chengye Wang, name: Weifeng Pan, name: Ziyao Shangguan, name: Xiangru Tang, name: Zhenwen Liang, name: Yixin Liu, name: Chen Zhao, name: Arman Cohan
Published: 2025-01-21 18:56:18

We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark
for evaluating foundation models in video understanding. MMVU includes 3,000
expert-annotated questions spanning 27 subjects across four core disciplines:
Science, Healthcare, Humanities & Social Sciences, and Engineering. Compared to
prior benchmarks, MMVU features three key advancements. First, it challenges
models to apply domain-specific knowledge and perform expert-level reasoning to
analyze specialized-domain videos, moving beyond the basic visual perception
typically assessed in current video benchmarks. Second, each example is
annotated by human experts from scratch. We implement strict data quality
controls to ensure the high quality of the dataset. Finally, each example is
enriched with expert-annotated reasoning rationals and relevant domain
knowledge, facilitating in-depth analysis. We conduct an extensive evaluation
of 32 frontier multimodal foundation models on MMVU. The latest
System-2-capable models, o1 and Gemini 2.0 Flash Thinking, achieve the highest
performance among the tested models. However, they still fall short of matching
human expertise. Through in-depth error analyses and case studies, we offer
actionable insights for future advancements in expert-level,
knowledge-intensive video understanding for specialized domains.
Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and
  Refinement
Authors: name: Maosong Cao, name: Taolin Zhang, name: Mo Li, name: Chuyu Zhang, name: Yunxin Liu, name: Haodong Duan, name: Songyang Zhang, name: Kai Chen
Published: 2025-01-21 16:44:12

The quality of Supervised Fine-Tuning (SFT) data plays a critical role in
enhancing the conversational capabilities of Large Language Models (LLMs).
However, as LLMs become more advanced, the availability of high-quality
human-annotated SFT data has become a significant bottleneck, necessitating a
greater reliance on synthetic training data. In this work, we introduce Condor,
a novel two-stage synthetic data generation framework that incorporates World
Knowledge Tree and Self-Reflection Refinement to produce high-quality SFT data
at scale. Our experimental results demonstrate that a base model fine-tuned on
only 20K Condor-generated samples achieves superior performance compared to
counterparts. The additional refinement stage in Condor further enables
iterative self-improvement for LLMs at various scales (up to 72B), validating
the effectiveness of our approach. Furthermore, our investigation into the
scaling for synthetic data in post-training reveals substantial unexplored
potential for performance improvements, opening promising avenues for future
research.
SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
Authors: name: Giyeong Oh, name: Saejin Kim, name: Woohyun Cho, name: Sangkyu Lee, name: Jiwan Chung, name: Dokyung Song, name: Youngjae Yu
Published: 2025-01-16 04:17:56

Recently, LoRA and its variants have become the de facto strategy for
training and sharing task-specific versions of large pretrained models, thanks
to their efficiency and simplicity. However, the issue of copyright protection
for LoRA weights, especially through watermark-based techniques, remains
underexplored. To address this gap, we propose SEAL (SEcure wAtermarking on
LoRA weights), the universal whitebox watermarking for LoRA. SEAL embeds a
secret, non-trainable matrix between trainable LoRA weights, serving as a
passport to claim ownership. SEAL then entangles the passport with the LoRA
weights through training, without extra loss for entanglement, and distributes
the finetuned weights after hiding the passport. When applying SEAL, we
observed no performance degradation across commonsense reasoning,
textual/visual instruction tuning, and text-to-image synthesis tasks. We
demonstrate that SEAL is robust against a variety of known attacks: removal,
obfuscation, and ambiguity attacks.
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Authors: name: Zhongwei Ren, name: Yunchao Wei, name: Xun Guo, name: Yao Zhao, name: Bingyi Kang, name: Jiashi Feng, name: Xiaojie Jin
Published: 2025-01-16 18:59:10

This work explores whether a deep generative model can learn complex
knowledge solely from visual input, in contrast to the prevalent focus on
text-based models like large language models (LLMs). We develop VideoWorld, an
auto-regressive video generation model trained on unlabeled video data, and
test its knowledge acquisition abilities in video-based Go and robotic control
tasks. Our experiments reveal two key findings: (1) video-only training
provides sufficient information for learning knowledge, including rules,
reasoning and planning capabilities, and (2) the representation of visual
change is crucial for knowledge acquisition. To improve both the efficiency and
efficacy of this process, we introduce the Latent Dynamics Model (LDM) as a key
component of VideoWorld. Remarkably, VideoWorld reaches a 5-dan professional
level in the Video-GoBench with just a 300-million-parameter model, without
relying on search algorithms or reward mechanisms typical in reinforcement
learning. In robotic tasks, VideoWorld effectively learns diverse control
operations and generalizes across environments, approaching the performance of
oracle models in CALVIN and RLBench. This study opens new avenues for knowledge
acquisition from visual data, with all code, data, and models open-sourced for
further research.
GameFactory: Creating New Games with Generative Interactive Videos
Authors: name: Jiwen Yu, name: Yiran Qin, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Xihui Liu
Published: 2025-01-14 18:57:21

Generative game engines have the potential to revolutionize game development
by autonomously creating new content and reducing manual workload. However,
existing video-based game generation methods fail to address the critical
challenge of scene generalization, limiting their applicability to existing
games with fixed styles and scenes. In this paper, we present GameFactory, a
framework focused on exploring scene generalization in game video generation.
To enable the creation of entirely new and diverse games, we leverage
pre-trained video diffusion models trained on open-domain video data. To bridge
the domain gap between open-domain priors and small-scale game dataset, we
propose a multi-phase training strategy that decouples game style learning from
action control, preserving open-domain generalization while achieving action
controllability. Using Minecraft as our data source, we release GF-Minecraft, a
high-quality and diversity action-annotated video dataset for research.
Furthermore, we extend our framework to enable autoregressive
action-controllable game video generation, allowing the production of
unlimited-length interactive game videos. Experimental results demonstrate that
GameFactory effectively generates open-domain, diverse, and action-controllable
game videos, representing a significant step forward in AI-driven game
generation. Our dataset and project page are publicly available at
https://vvictoryuki.github.io/gamefactory/.
Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
Authors: name: Nada Saadi, name: Tathagata Raha, name: Clément Christophe, name: Marco AF Pimentel, name: Ronnie Rajan, name: Praveen K Kanithi
Published: 2025-01-16 20:24:56

This paper investigates the challenges of developing large language models
(LLMs) proficient in both multilingual understanding and medical knowledge. We
demonstrate that simply translating medical data does not guarantee strong
performance on clinical tasks in the target language. Our experiments reveal
that the optimal language mix in training data varies significantly across
different medical tasks. We find that larger models with carefully calibrated
language ratios achieve superior performance on native-language clinical tasks.
Furthermore, our results suggest that relying solely on fine-tuning may not be
the most effective approach for incorporating new language knowledge into LLMs.
Instead, data and computationally intensive pretraining methods may still be
necessary to achieve optimal performance in multilingual medical settings.
These findings provide valuable guidance for building effective and inclusive
medical AI systems for diverse linguistic communities.
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs)
  More Self-Confident Even When They Are Wrong
Authors: name: Tairan Fu, name: Javier Conde, name: Gonzalo Martínez, name: María Grandury, name: Pedro Reviriego
Published: 2025-01-16 10:27:51

One of the most widely used methods to evaluate LLMs are Multiple Choice
Question (MCQ) tests. MCQ benchmarks enable the testing of LLM knowledge on
almost any topic at scale as the results can be processed automatically. To
help the LLM answer, a few examples called few shots can be included in the
prompt. Moreover, the LLM can be asked to answer the question directly with the
selected option or to first provide the reasoning and then the selected answer,
which is known as chain of thought. In addition to checking whether the
selected answer is correct, the evaluation can look at the LLM-estimated
probability of its response as an indication of the confidence of the LLM in
the response. In this paper, we study how the LLM confidence in its answer
depends on whether the model has been asked to answer directly or to provide
the reasoning before answering. The results of the evaluation of questions on a
wide range of topics in seven different models show that LLMs are more
confident in their answers when they provide reasoning before the answer. This
occurs regardless of whether the selected answer is correct. Our hypothesis is
that this behavior is due to the reasoning that modifies the probability of the
selected answer, as the LLM predicts the answer based on the input question and
the reasoning that supports the selection made. Therefore, LLM estimated
probabilities seem to have intrinsic limitations that should be understood in
order to use them in evaluation procedures. Interestingly, the same behavior
has been observed in humans, for whom explaining an answer increases confidence
in its correctness.
X-Dyna: Expressive Dynamic Human Image Animation
Authors: name: Di Chang, name: Hongyi Xu, name: You Xie, name: Yipeng Gao, name: Zhengfei Kuang, name: Shengqu Cai, name: Chenxu Zhang, name: Guoxian Song, name: Chao Wang, name: Yichun Shi, name: Zeyuan Chen, name: Shijie Zhou, name: Linjie Luo, name: Gordon Wetzstein, name: Mohammad Soleymani
Published: 2025-01-17 08:10:53

We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for
animating a single human image using facial expressions and body movements
derived from a driving video, that generates realistic, context-aware dynamics
for both the subject and the surrounding environment. Building on prior
approaches centered on human pose control, X-Dyna addresses key shortcomings
causing the loss of dynamic details, enhancing the lifelike qualities of human
video animations. At the core of our approach is the Dynamics-Adapter, a
lightweight module that effectively integrates reference appearance context
into the spatial attentions of the diffusion backbone while preserving the
capacity of motion modules in synthesizing fluid and intricate dynamic details.
Beyond body pose control, we connect a local control module with our model to
capture identity-disentangled facial expressions, facilitating accurate
expression transfer for enhanced realism in animated scenes. Together, these
components form a unified framework capable of learning physical human motion
and natural scene dynamics from a diverse blend of human and scene videos.
Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna
outperforms state-of-the-art methods, creating highly lifelike and expressive
animations. The code is available at https://github.com/bytedance/X-Dyna.
HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial
  Network for High-Fidelity Speech Super-Resolution
Authors: name: Shengkui Zhao, name: Kun Zhou, name: Zexu Pan, name: Yukun Ma, name: Chong Zhang, name: Bin Ma
Published: 2025-01-17 09:04:38

The application of generative adversarial networks (GANs) has recently
advanced speech super-resolution (SR) based on intermediate representations
like mel-spectrograms. However, existing SR methods that typically rely on
independently trained and concatenated networks may lead to inconsistent
representations and poor speech quality, especially in out-of-domain scenarios.
In this work, we propose HiFi-SR, a unified network that leverages end-to-end
adversarial training to achieve high-fidelity speech super-resolution. Our
model features a unified transformer-convolutional generator designed to
seamlessly handle both the prediction of latent representations and their
conversion into time-domain waveforms. The transformer network serves as a
powerful encoder, converting low-resolution mel-spectrograms into latent space
representations, while the convolutional network upscales these representations
into high-resolution waveforms. To enhance high-frequency fidelity, we
incorporate a multi-band, multi-scale time-frequency discriminator, along with
a multi-scale mel-reconstruction loss in the adversarial training process.
HiFi-SR is versatile, capable of upscaling any input speech signal between 4
kHz and 32 kHz to a 48 kHz sampling rate. Experimental results demonstrate that
HiFi-SR significantly outperforms existing speech SR methods across both
objective metrics and ABX preference tests, for both in-domain and
out-of-domain scenarios (https://github.com/modelscope/ClearerVoice-Studio).
Textoon: Generating Vivid 2D Cartoon Characters from Text Descriptions
Authors: name: Chao He, name: Jianqiang Ren, name: Liefeng Bo
Published: 2025-01-17 08:09:06

The 2D cartoon style is a prominent art form in digital character creation,
particularly popular among younger audiences. While advancements in digital
human technology have spurred extensive research into photorealistic digital
humans and 3D characters, interactive 2D cartoon characters have received
comparatively less attention. Unlike 3D counterparts, which require
sophisticated construction and resource-intensive rendering, Live2D, a
widely-used format for 2D cartoon characters, offers a more efficient
alternative, which allows to animate 2D characters in a manner that simulates
3D movement without the necessity of building a complete 3D model. Furthermore,
Live2D employs lightweight HTML5 (H5) rendering, improving both accessibility
and efficiency. In this technical report, we introduce Textoon, an innovative
method for generating diverse 2D cartoon characters in the Live2D format based
on text descriptions. The Textoon leverages cutting-edge language and vision
models to comprehend textual intentions and generate 2D appearance, capable of
creating a wide variety of stunning and interactive 2D characters within one
minute. The project homepage is https://human3daigc.github.io/Textoon_webpage/.
GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
  Editor
Authors: name: Xiangyue Liu, name: Kunming Luo, name: Heng Li, name: Qi Zhang, name: Yuan Liu, name: Li Yi, name: Ping Tan
Published: 2025-01-17 06:40:20

We introduce GaussianAvatar-Editor, an innovative framework for text-driven
editing of animatable Gaussian head avatars that can be fully controlled in
expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing
animatable 4D Gaussian avatars presents challenges related to motion occlusion
and spatial-temporal inconsistency. To address these issues, we propose the
Weighted Alpha Blending Equation (WABE). This function enhances the blending
weight of visible Gaussians while suppressing the influence on non-visible
Gaussians, effectively handling motion occlusion during editing. Furthermore,
to improve editing quality and ensure 4D consistency, we incorporate
conditional adversarial learning into the editing process. This strategy helps
to refine the edited results and maintain consistency throughout the animation.
By integrating these methods, our GaussianAvatar-Editor achieves photorealistic
and consistent results in animatable 4D Gaussian editing. We conduct
comprehensive experiments across various subjects to validate the effectiveness
of our proposed techniques, which demonstrates the superiority of our approach
over existing methods. More results and code are available at: [Project
Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).
Evolving Deeper LLM Thinking
Authors: name: Kuang-Huei Lee, name: Ian Fischer, name: Yueh-Hua Wu, name: Dave Marwood, name: Shumeet Baluja, name: Dale Schuurmans, name: Xinyun Chen
Published: 2025-01-17 00:41:44

We explore an evolutionary search strategy for scaling inference time compute
in Large Language Models. The proposed approach, Mind Evolution, uses a
language model to generate, recombine and refine candidate responses. The
proposed approach avoids the need to formalize the underlying inference problem
whenever a solution evaluator is available. Controlling for inference cost, we
find that Mind Evolution significantly outperforms other inference strategies
such as Best-of-N and Sequential Revision in natural language planning tasks.
In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more
than 98% of the problem instances using Gemini 1.5 Pro without the use of a
formal solver.