Orionnnn/hub-stats huggingface dataset:
AMD-Hummingbird: Towards an Efficient Text-to-Video Model
Authors: name: Takashi Isobe, name: He Cui, name: Dong Zhou, name: Mengmeng Ge, name: Dong Li, name: Emad Barsoum
Published: 2025-03-24 11:13:33

Text-to-Video (T2V) generation has attracted significant attention for its
ability to synthesize realistic videos from textual descriptions. However,
existing models struggle to balance computational efficiency and high visual
quality, particularly on resource-limited devices, e.g.,iGPUs and mobile
phones. Most prior work prioritizes visual fidelity while overlooking the need
for smaller, more efficient models suitable for real-world deployment. To
address this challenge, we propose a lightweight T2V framework, termed
Hummingbird, which prunes existing models and enhances visual quality through
visual feedback learning. Our approach reduces the size of the U-Net from 1.4
billion to 0.7 billion parameters, significantly improving efficiency while
preserving high-quality video generation. Additionally, we introduce a novel
data processing pipeline that leverages Large Language Models (LLMs) and Video
Quality Assessment (VQA) models to enhance the quality of both text prompts and
video data. To support user-driven training and style customization, we
publicly release the full training code, including data processing and model
training. Extensive experiments show that our method achieves a 31X speedup
compared to state-of-the-art models such as VideoCrafter2, while also attaining
the highest overall score on VBench. Moreover, our method supports the
generation of videos with up to 26 frames, addressing the limitations of
existing U-Net-based methods in long video generation. Notably, the entire
training process requires only four GPUs, yet delivers performance competitive
with existing leading methods. Hummingbird presents a practical and efficient
solution for T2V generation, combining high performance, scalability, and
flexibility for real-world applications.
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for
  Open Base Models in the Wild
Authors: name: Weihao Zeng, name: Yuzhen Huang, name: Qian Liu, name: Wei Liu, name: Keqing He, name: Zejun Ma, name: Junxian He
Published: 2025-03-24 17:06:10

DeepSeek-R1 has shown that long chain-of-thought (CoT) reasoning can
naturally emerge through a simple reinforcement learning (RL) framework with
rule-based rewards, where the training may directly start from the base
models-a paradigm referred to as zero RL training. Most recent efforts to
reproduce zero RL training have primarily focused on the Qwen2.5 model series,
which may not be representative as we find the base models already exhibit
strong instruction-following and self-reflection abilities. In this work, we
investigate zero RL training across 10 diverse base models, spanning different
families and sizes including LLama3-8B, Mistral-7B/24B, DeepSeek-Math-7B,
Qwen2.5-math-7B, and all Qwen2.5 models from 0.5B to 32B. Leveraging several
key design strategies-such as adjusting format reward and controlling query
difficulty-we achieve substantial improvements in both reasoning accuracy and
response length across most settings. However, by carefully monitoring the
training dynamics, we observe that different base models exhibit distinct
patterns during training. For instance, the increased response length does not
always correlate with the emergence of certain cognitive behaviors such as
verification (i.e., the "aha moment"). Notably, we observe the "aha moment" for
the first time in small models not from the Qwen family. We share the key
designs that enable successful zero RL training, along with our findings and
practices. To facilitate further research, we open-source the code, models, and
analysis tools.
RDTF: Resource-efficient Dual-mask Training Framework for Multi-frame
  Animated Sticker Generation
Authors: name: Zhiqiang Yuan, name: Ting Zhang, name: Ying Deng, name: Jiapei Zhang, name: Yeshuang Zhu, name: Zexi Jia, name: Jie Zhou, name: Jinchao Zhang
Published: 2025-03-22 11:28:25

Recently, great progress has been made in video generation technology,
attracting the widespread attention of scholars. To apply this technology to
downstream applications under resource-constrained conditions, researchers
usually fine-tune the pre-trained models based on parameter-efficient tuning
methods such as Adapter or Lora. Although these methods can transfer the
knowledge from the source domain to the target domain, fewer training
parameters lead to poor fitting ability, and the knowledge from the source
domain may lead to the inference process deviating from the target domain. In
this paper, we argue that under constrained resources, training a smaller video
generation model from scratch using only million-level samples can outperform
parameter-efficient tuning on larger models in downstream applications: the
core lies in the effective utilization of data and curriculum strategy. Take
animated sticker generation (ASG) as a case study, we first construct a
discrete frame generation network for stickers with low frame rates, ensuring
that its parameters meet the requirements of model training under constrained
resources. In order to provide data support for models trained from scratch, we
come up with a dual-mask based data utilization strategy, which manages to
improve the availability and expand the diversity of limited data. To
facilitate convergence under dual-mask situation, we propose a
difficulty-adaptive curriculum learning method, which decomposes the sample
entropy into static and adaptive components so as to obtain samples from easy
to difficult. The experiment demonstrates that our resource-efficient dual-mask
training framework is quantitatively and qualitatively superior to
efficient-parameter tuning methods such as I2V-Adapter and SimDA, verifying the
feasibility of our method on downstream tasks under constrained resources. Code
will be available.
Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models
  via Vision-Guided Reinforcement Learning
Authors: name: Yufei Zhan, name: Yousong Zhu, name: Shurong Zheng, name: Hongyin Zhao, name: Fan Yang, name: Ming Tang, name: Jinqiao Wang
Published: 2025-03-23 10:21:14

Large Vision-Language Models (LVLMs) typically follow a two-stage training
paradigm-pretraining and supervised fine-tuning. Recently, preference
optimization, derived from the language domain, has emerged as an effective
post-training reinforcement strategy to enhance capabilities of LVLMs. However,
constructing high-quality human-annotated preference data and developing robust
reward models to mimic these preferences are both costly and challenging.
Motivated by this observation, we propose Vision-R1, a novel vision-guided
R1-like reinforcement learning algorithm for LVLMs that rewards models with
definitive vision feedback. It only leverages curated instruction data,
eliminating the need for specialized reward models and handcrafted preference
datasets. We incorporate a criterion-driven reward function that further
integrates multi-dimensional feedback to evaluate model completions
comprehensively based on the vision task logic. Furthermore, we introduce a
progressive rule refinement strategy that dynamically adjusts the reward
criteria during training, enabling continuous model improvement and mitigating
reward hacking. Extensive experiments on both in-distribution and
out-of-distribution benchmarks demonstrate that fine-tuning the 7B LVLMs with
Vision-R1 achieves consistent performance gains, with even up to 50%
improvement and surpassing the state-of-the-art 10x size model.
Reasoning to Learn from Latent Thoughts
Authors: name: Yangjun Ruan, name: Neil Band, name: Chris J. Maddison, name: Tatsunori Hashimoto
Published: 2025-03-24 16:41:23

Compute scaling for language model (LM) pretraining has outpaced the growth
of human-written texts, leading to concerns that data will become the
bottleneck to LM scaling. To continue scaling pretraining in this
data-constrained regime, we propose that explicitly modeling and inferring the
latent thoughts that underlie the text generation process can significantly
improve pretraining data efficiency. Intuitively, our approach views web text
as the compressed final outcome of a verbose human thought process and that the
latent thoughts contain important contextual knowledge and reasoning steps that
are critical to data-efficient learning. We empirically demonstrate the
effectiveness of our approach through data-constrained continued pretraining
for math. We first show that synthetic data approaches to inferring latent
thoughts significantly improve data efficiency, outperforming training on the
same amount of raw data (5.7\% rightarrow 25.4\% on MATH). Furthermore, we
demonstrate latent thought inference without a strong teacher, where an LM
bootstraps its own performance by using an EM algorithm to iteratively improve
the capability of the trained LM and the quality of thought-augmented
pretraining data. We show that a 1B LM can bootstrap its performance across at
least three iterations and significantly outperform baselines trained on raw
data, with increasing gains from additional inference compute when performing
the E-step. The gains from inference scaling and EM iterations suggest new
opportunities for scaling data-constrained pretraining.
Video SimpleQA: Towards Factuality Evaluation in Large Video Language
  Models
Authors: name: Meng Cao, name: Pengfei Hu, name: Yingyao Wang, name: Jihao Gu, name: Haoran Tang, name: Haoze Zhao, name: Jiahua Dong, name: Wangbo Yu, name: Ge Zhang, name: Ian Reid, name: Xiaodan Liang
Published: 2025-03-24 17:46:09

Recent advancements in Large Video Language Models (LVLMs) have highlighted
their potential for multi-modal understanding, yet evaluating their factual
grounding in video contexts remains a critical unsolved challenge. To address
this gap, we introduce Video SimpleQA, the first comprehensive benchmark
tailored for factuality evaluation of LVLMs. Our work distinguishes from
existing video benchmarks through the following key features: 1) Knowledge
required: demanding integration of external knowledge beyond the explicit
narrative; 2) Fact-seeking question: targeting objective, undisputed events or
relationships, avoiding subjective interpretation; 3) Definitive & short-form
answer: Answers are crafted as unambiguous and definitively correct in a short
format, enabling automated evaluation through LLM-as-a-judge frameworks with
minimal scoring variance; 4) External-source verified: All annotations undergo
rigorous validation against authoritative external references to ensure the
reliability; 5) Temporal reasoning required: The annotated question types
encompass both static single-frame understanding and dynamic temporal
reasoning, explicitly evaluating LVLMs factuality under the long-context
dependencies. We extensively evaluate 41 state-of-the-art LVLMs and summarize
key findings as follows: 1) Current LVLMs exhibit notable deficiencies in
factual adherence, particularly for open-source models. The best-performing
model Gemini-1.5-Pro achieves merely an F-score of 54.4%; 2) Test-time compute
paradigms show insignificant performance gains, revealing fundamental
constraints for enhancing factuality through post-hoc computation; 3)
Retrieval-Augmented Generation demonstrates consistent improvements at the cost
of additional inference time overhead, presenting a critical
efficiency-performance trade-off.
V-Seek: Accelerating LLM Reasoning on Open-hardware Server-class RISC-V
  Platforms
Authors: name: Javier J. Poveda Rodrigo, name: Mohamed Amine Ahmdi, name: Alessio Burrello, name: Daniele Jahier Pagliari, name: Luca Benini
Published: 2025-03-21 09:00:19

The recent exponential growth of Large Language Models (LLMs) has relied on
GPU-based systems. However, CPUs are emerging as a flexible and lower-cost
alternative, especially when targeting inference and reasoning workloads.
RISC-V is rapidly gaining traction in this area, given its open and
vendor-neutral ISA. However, the RISC-V hardware for LLM workloads and the
corresponding software ecosystem are not fully mature and streamlined, given
the requirement of domain-specific tuning. This paper aims at filling this gap,
focusing on optimizing LLM inference on the Sophon SG2042, the first
commercially available many-core RISC-V CPU with vector processing
capabilities.
  On two recent state-of-the-art LLMs optimized for reasoning, DeepSeek R1
Distill Llama 8B and DeepSeek R1 Distill QWEN 14B, we achieve 4.32/2.29 token/s
for token generation and 6.54/3.68 token/s for prompt processing, with a speed
up of up 2.9x/3.0x compared to our baseline.
Video-T1: Test-Time Scaling for Video Generation
Authors: name: Fangfu Liu, name: Hanyang Wang, name: Yimo Cai, name: Kaiyan Zhang, name: Xiaohang Zhan, name: Yueqi Duan
Published: 2025-03-24 17:59:04

With the scale capability of increasing training data, model size, and
computational cost, video generation has achieved impressive results in digital
creation, enabling users to express creativity across various domains.
Recently, researchers in Large Language Models (LLMs) have expanded the scaling
to test-time, which can significantly improve LLM performance by using more
inference-time computation. Instead of scaling up video foundation models
through expensive training costs, we explore the power of Test-Time Scaling
(TTS) in video generation, aiming to answer the question: if a video generation
model is allowed to use non-trivial amount of inference-time compute, how much
can it improve generation quality given a challenging text prompt. In this
work, we reinterpret the test-time scaling of video generation as a searching
problem to sample better trajectories from Gaussian noise space to the target
video distribution. Specifically, we build the search space with test-time
verifiers to provide feedback and heuristic algorithms to guide searching
process. Given a text prompt, we first explore an intuitive linear search
strategy by increasing noise candidates at inference time. As full-step
denoising all frames simultaneously requires heavy test-time computation costs,
we further design a more efficient TTS method for video generation called
Tree-of-Frames (ToF) that adaptively expands and prunes video branches in an
autoregressive manner. Extensive experiments on text-conditioned video
generation benchmarks demonstrate that increasing test-time compute
consistently leads to significant improvements in the quality of videos.
Project page: https://liuff19.github.io/Video-T1
LEMMA: Learning from Errors for MatheMatical Advancement in LLMs
Authors: name: Zhuoshi Pan, name: Yu Li, name: Honglin Lin, name: Qizhi Pei, name: Zinan Tang, name: Wei Wu, name: Chenlin Ming, name: H. Vicky Zhao, name: Conghui He, name: Lijun Wu
Published: 2025-03-21 17:59:10

Large language models (LLMs) have demonstrated remarkable reasoning
capability in solving mathematical problems. However, existing approaches
primarily focus on improving the quality of correct training data, e.g.,
distilling high-quality correct solutions from advanced models, neglecting the
value contained in error data, potentially hindering the model's reflective
ability. Though some studies attempt to leverage error data, they often involve
complex mechanisms, such as Monte Carlo Tree Search (MCTS) to explore error
nodes. In this work, we propose to enhance LLMs' reasoning ability by Learning
from Errors for Mathematical Advancement (LEMMA). LEMMA constructs data
consisting of an incorrect solution with an erroneous step and a reflection
connection to a correct solution for fine-tuning. Specifically, we
systematically analyze the model-generated error types and introduce an
error-type grounded mistake augmentation method to collect diverse and
representative errors. Correct solutions are either from fixing the errors or
generating a fresh start. Through a model-aware smooth reflection connection,
the erroneous solution is transferred to the correct one. By fine-tuning on the
constructed dataset, the model is able to self-correct errors autonomously
within the generation process without relying on external critique models.
Experimental results demonstrate that LEMMA achieves significant performance
improvements over other strong baselines.
Judge Anything: MLLM as a Judge Across Any Modality
Authors: name: Shu Pu, name: Yaochen Wang, name: Dongping Chen, name: Yuhang Chen, name: Guohao Wang, name: Qi Qin, name: Zhongyi Zhang, name: Zhiyuan Zhang, name: Zetong Zhou, name: Shuang Gong, name: Yi Gui, name: Yao Wan, name: Philip S. Yu
Published: 2025-03-21 18:59:20

Evaluating generative foundation models on open-ended multimodal
understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g.,
images, audio, video) poses significant challenges due to the complexity of
cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs
(MLLMs) as automated judges has emerged, with encouraging results in assessing
vision-language understanding tasks. Moving further, this paper extends
MLLM-as-a-Judge across modalities to a unified manner by introducing two
benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the
overall performance and judging capabilities of MLLMs across any-to-any
modality tasks. Specifically, TaskAnything evaluates the MMU and MMG
capabilities across 15 any-to-any modality categories, employing 1,500 queries
curated from well-established benchmarks. Furthermore, JudgeAnything evaluates
the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from
the perspectives of Pair Comparison and Score Evaluation, providing a
standardized testbed that incorporates human judgments and detailed rubrics.
Our extensive experiments reveal that while these MLLMs show promise in
assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting
and 42.79% in Score Evaluation setting), they encounter significant challenges
with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and
30.05% in Score Evaluation setting), exposing cross-modality biases and
hallucination issues. To address this, we present OmniArena, an automated
platform for evaluating omni-models and multimodal reward models. Our work
highlights the need for fairer evaluation protocols and stronger alignment with
human preferences. The source code and dataset are publicly available at:
https://urrealhero.github.io/judgeanythingweb/.
Position: Interactive Generative Video as Next-Generation Game Engine
Authors: name: Jiwen Yu, name: Yiran Qin, name: Haoxuan Che, name: Quande Liu, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Xihui Liu
Published: 2025-03-21 17:59:22

Modern game development faces significant challenges in creativity and cost
due to predetermined content in traditional game engines. Recent breakthroughs
in video generation models, capable of synthesizing realistic and interactive
virtual environments, present an opportunity to revolutionize game creation. In
this position paper, we propose Interactive Generative Video (IGV) as the
foundation for Generative Game Engines (GGE), enabling unlimited novel content
generation in next-generation gaming. GGE leverages IGV's unique strengths in
unlimited high-quality content synthesis, physics-aware world modeling,
user-controlled interactivity, long-term memory capabilities, and causal
reasoning. We present a comprehensive framework detailing GGE's core modules
and a hierarchical maturity roadmap (L0-L4) to guide its evolution. Our work
charts a new course for game development in the AI era, envisioning a future
where AI-powered generative systems fundamentally reshape how games are created
and experienced.
Training-free Diffusion Acceleration with Bottleneck Sampling
Authors: name: Ye Tian, name: Xin Xia, name: Yuxi Ren, name: Shanchuan Lin, name: Xing Wang, name: Xuefeng Xiao, name: Yunhai Tong, name: Ling Yang, name: Bin Cui
Published: 2025-03-24 17:59:02

Diffusion models have demonstrated remarkable capabilities in visual content
generation but remain challenging to deploy due to their high computational
cost during inference. This computational burden primarily arises from the
quadratic complexity of self-attention with respect to image or video
resolution. While existing acceleration methods often compromise output quality
or necessitate costly retraining, we observe that most diffusion models are
pre-trained at lower resolutions, presenting an opportunity to exploit these
low-resolution priors for more efficient inference without degrading
performance. In this work, we introduce Bottleneck Sampling, a training-free
framework that leverages low-resolution priors to reduce computational overhead
while preserving output fidelity. Bottleneck Sampling follows a high-low-high
denoising workflow: it performs high-resolution denoising in the initial and
final stages while operating at lower resolutions in intermediate steps. To
mitigate aliasing and blurring artifacts, we further refine the resolution
transition points and adaptively shift the denoising timesteps at each stage.
We evaluate Bottleneck Sampling on both image and video generation tasks, where
extensive experiments demonstrate that it accelerates inference by up to
3times for image generation and 2.5times for video generation, all while
maintaining output quality comparable to the standard full-resolution sampling
process across multiple evaluation metrics. Code is available at:
https://github.com/tyfeld/Bottleneck-Sampling
AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and
  Symbolic Reasoning
Authors: name: Alan Dao, name: Dinh Bach Vu, name: Bui Quang Huy
Published: 2025-03-24 15:16:51

This paper presents AlphaSpace, a novel methodology designed to enhance the
spatial reasoning capabilities of large language models (LLMs) for 3D Cartesian
space navigation. AlphaSpace employs a semantics-based tokenization strategy,
encoding height information through specialized semantic tokens, and integrates
primarily symbolic synthetic reasoning data. This approach enables LLMs to
accurately manipulate objects by positioning them at specific [x, y, z]
coordinates. Experimental results demonstrate that AlphaSpace significantly
outperforms existing models on manipulation subtasks, achieving a total
accuracy of 66.67%, compared to 37.5% for GPT-4o and 29.17% for Claude 3.5
Sonnet.
MagicComp: Training-free Dual-Phase Refinement for Compositional Video
  Generation
Authors: name: Hongyu Zhang, name: Yufan Deng, name: Shenghai Yuan, name: Peng Jin, name: Zesen Cheng, name: Yian Zhao, name: Chang Liu, name: Jie Chen
Published: 2025-03-18 17:02:14

Text-to-video (T2V) generation has made significant strides with diffusion
models. However, existing methods still struggle with accurately binding
attributes, determining spatial relationships, and capturing complex action
interactions between multiple subjects. To address these limitations, we
propose MagicComp, a training-free method that enhances compositional T2V
generation through dual-phase refinement. Specifically, (1) During the
Conditioning Stage: We introduce the Semantic Anchor Disambiguation to
reinforces subject-specific semantics and resolve inter-subject ambiguity by
progressively injecting the directional vectors of semantic anchors into
original text embedding; (2) During the Denoising Stage: We propose Dynamic
Layout Fusion Attention, which integrates grounding priors and model-adaptive
spatial perception to flexibly bind subjects to their spatiotemporal regions
through masked attention modulation. Furthermore, MagicComp is a model-agnostic
and versatile approach, which can be seamlessly integrated into existing T2V
architectures. Extensive experiments on T2V-CompBench and VBench demonstrate
that MagicComp outperforms state-of-the-art methods, highlighting its potential
for applications such as complex prompt-based and trajectory-controllable video
generation. Project page: https://hong-yu-zhang.github.io/MagicComp-Page/.