Orionnnn/hub-stats huggingface dataset:
AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong
  Pretraining Data Selection
Authors: name: Kai Hua, name: Steven Wu, name: Ge Zhang, name: Ke Shen
Published: 2025-05-12 07:25:51

Recently, there has been growing interest in collecting reasoning-intensive
pretraining data to improve LLMs' complex reasoning ability. Prior approaches
typically rely on supervised classifiers to identify such data, which requires
labeling by humans or LLMs, often introducing domain-specific biases. Due to
the attention heads being crucial to in-context reasoning, we propose
AttentionInfluence, a simple yet effective, training-free method without
supervision signal. Our approach enables a small pretrained language model to
act as a strong data selector through a simple attention head masking
operation. Specifically, we identify retrieval heads and compute the loss
difference when masking these heads. We apply AttentionInfluence to a
1.3B-parameter dense model to conduct data selection on the SmolLM corpus of
241B tokens, and mix the SmolLM corpus with the selected subset comprising 73B
tokens to pretrain a 7B-parameter dense model using 1T training tokens and WSD
learning rate scheduling. Our experimental results demonstrate substantial
improvements, ranging from 1.4pp to 3.5pp, across several knowledge-intensive
and reasoning-heavy benchmarks (i.e., MMLU, MMLU-Pro, AGIEval-en, GSM8K, and
HumanEval). This demonstrates an effective weak-to-strong scaling property,
with small models improving the final performance of larger models-offering a
promising and scalable path for reasoning-centric data selection.
Unified Continuous Generative Models
Authors: name: Peng Sun, name: Yi Jiang, name: Tao Lin
Published: 2025-05-12 11:15:39

Recent advances in continuous generative models, including multi-step
approaches like diffusion and flow-matching (typically requiring 8-1000
sampling steps) and few-step methods such as consistency models (typically 1-8
steps), have demonstrated impressive generative performance. However, existing
work often treats these approaches as distinct paradigms, resulting in separate
training and sampling methodologies. We introduce a unified framework for
training, sampling, and analyzing these models. Our implementation, the Unified
Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves
state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a
675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID
in 20 steps and a few-step model reaching 1.42 FID in just 2 steps.
Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at
250 steps) improves performance to 1.06 FID in only 40 steps. Code is available
at: https://github.com/LINs-lab/UCGM.
Multi-Objective-Guided Discrete Flow Matching for Controllable
  Biological Sequence Design
Authors: name: Tong Chen, name: Yinuo Zhang, name: Sophia Tang, name: Pranam Chatterjee
Published: 2025-05-11 18:17:44

Designing biological sequences that satisfy multiple, often conflicting,
functional and biophysical criteria remains a central challenge in biomolecule
engineering. While discrete flow matching models have recently shown promise
for efficient sampling in high-dimensional sequence spaces, existing approaches
address only single objectives or require continuous embeddings that can
distort discrete distributions. We present Multi-Objective-Guided Discrete Flow
Matching (MOG-DFM), a general framework to steer any pretrained discrete-time
flow matching generator toward Pareto-efficient trade-offs across multiple
scalar objectives. At each sampling step, MOG-DFM computes a hybrid
rank-directional score for candidate transitions and applies an adaptive
hypercone filter to enforce consistent multi-objective progression. We also
trained two unconditional discrete flow matching models, PepDFM for diverse
peptide generation and EnhancerDFM for functional enhancer DNA generation, as
base generation models for MOG-DFM. We demonstrate MOG-DFM's effectiveness in
generating peptide binders optimized across five properties (hemolysis,
non-fouling, solubility, half-life, and binding affinity), and in designing DNA
sequences with specific enhancer classes and DNA shapes. In total, MOG-DFM
proves to be a powerful tool for multi-property-guided biomolecule sequence
design.
Document Attribution: Examining Citation Relationships using Large
  Language Models
Authors: name: Vipula Rawte, name: Ryan A. Rossi, name: Franck Dernoncourt, name: Nedim Lipka
Published: 2025-05-09 04:40:11

As Large Language Models (LLMs) are increasingly applied to document-based
tasks - such as document summarization, question answering, and information
extraction - where user requirements focus on retrieving information from
provided documents rather than relying on the model's parametric knowledge,
ensuring the trustworthiness and interpretability of these systems has become a
critical concern. A central approach to addressing this challenge is
attribution, which involves tracing the generated outputs back to their source
documents. However, since LLMs can produce inaccurate or imprecise responses,
it is crucial to assess the reliability of these citations.
  To tackle this, our work proposes two techniques. (1) A zero-shot approach
that frames attribution as a straightforward textual entailment task. Our
method using flan-ul2 demonstrates an improvement of 0.27% and 2.4% over the
best baseline of ID and OOD sets of AttributionBench, respectively. (2) We also
explore the role of the attention mechanism in enhancing the attribution
process. Using a smaller LLM, flan-t5-small, the F1 scores outperform the
baseline across almost all layers except layer 4 and layers 8 through 11.
Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured
  3D Assets
Authors: name: Weiyu Li, name: Xuanyang Zhang, name: Zheng Sun, name: Di Qi, name: Hao Li, name: Wei Cheng, name: Weiwei Cai, name: Shihao Wu, name: Jiarui Liu, name: Zihao Wang, name: Xiao Chen, name: Feipeng Tian, name: Jianxiong Pan, name: Zeming Li, name: Gang Yu, name: Xiangyu Zhang, name: Daxin Jiang, name: Ping Tan
Published: 2025-05-12 16:56:30

While generative artificial intelligence has advanced significantly across
text, image, audio, and video domains, 3D generation remains comparatively
underdeveloped due to fundamental challenges such as data scarcity, algorithmic
limitations, and ecosystem fragmentation. To this end, we present Step1X-3D, an
open framework addressing these challenges through: (1) a rigorous data
curation pipeline processing >5M assets to create a 2M high-quality dataset
with standardized geometric and textural properties; (2) a two-stage 3D-native
architecture combining a hybrid VAE-DiT geometry generator with an
diffusion-based texture synthesis module; and (3) the full open-source release
of models, training code, and adaptation modules. For geometry generation, the
hybrid VAE-DiT component produces TSDF representations by employing
perceiver-based latent encoding with sharp edge sampling for detail
preservation. The diffusion-based texture synthesis module then ensures
cross-view consistency through geometric conditioning and latent-space
synchronization. Benchmark results demonstrate state-of-the-art performance
that exceeds existing open-source methods, while also achieving competitive
quality with proprietary solutions. Notably, the framework uniquely bridges the
2D and 3D generation paradigms by supporting direct transfer of 2D control
techniques~(e.g., LoRA) to 3D synthesis. By simultaneously advancing data
quality, algorithmic fidelity, and reproducibility, Step1X-3D aims to establish
new standards for open research in controllable 3D asset generation.
Reinforced Internal-External Knowledge Synergistic Reasoning for
  Efficient Adaptive Search Agent
Authors: name: Ziyang Huang, name: Xiaowei Yuan, name: Yiming Ju, name: Jun Zhao, name: Kang Liu
Published: 2025-05-12 14:21:57

Retrieval-augmented generation (RAG) is a common strategy to reduce
hallucinations in Large Language Models (LLMs). While reinforcement learning
(RL) can enable LLMs to act as search agents by activating retrieval
capabilities, existing ones often underutilize their internal knowledge. This
can lead to redundant retrievals, potential harmful knowledge conflicts, and
increased inference latency. To address these limitations, an efficient and
adaptive search agent capable of discerning optimal retrieval timing and
synergistically integrating parametric (internal) and retrieved (external)
knowledge is in urgent need. This paper introduces the Reinforced
Internal-External Knowledge Synergistic Reasoning Agent (IKEA), which could
indentify its own knowledge boundary and prioritize the utilization of internal
knowledge, resorting to external search only when internal knowledge is deemed
insufficient. This is achieved using a novel knowledge-boundary aware reward
function and a knowledge-boundary aware training dataset. These are designed
for internal-external knowledge synergy oriented RL, incentivizing the model to
deliver accurate answers, minimize unnecessary retrievals, and encourage
appropriate external searches when its own knowledge is lacking. Evaluations
across multiple knowledge reasoning tasks demonstrate that IKEA significantly
outperforms baseline methods, reduces retrieval frequency significantly, and
exhibits robust generalization capabilities.
MiMo: Unlocking the Reasoning Potential of Language Model -- From
  Pretraining to Posttraining
Authors: name: Xiaomi LLM-Core Team, name: Bingquan Xia, name: Bowen Shen, name: Cici, name: Dawei Zhu, name: Di Zhang, name: Gang Wang, name: Hailin Zhang, name: Huaqiu Liu, name: Jiebao Xiao, name: Jinhao Dong, name: Liang Zhao, name: Peidian Li, name: Peng Wang, name: Shihua Yu, name: Shimao Chen, name: Weikun Wang, name: Wenhan Ma, name: Xiangwei Deng, name: Yi Huang, name: Yifan Song, name: Zihan Jiang, name: Bowen Ye, name: Can Cai, name: Chenhong He, name: Dong Zhang, name: Duo Zhang, name: Guoan Wang, name: Hao Tian, name: Haochen Zhao, name: Heng Qu, name: Hongshen Xu, name: Jun Shi, name: Kainan Bao, name: QingKai Fang, name: Kang Zhou, name: Kangyang Zhou, name: Lei Li, name: Menghang Zhu, name: Nuo Chen, name: Qiantong Wang, name: Shaohui Liu, name: Shicheng Li, name: Shuhao Gu, name: Shuhuai Ren, name: Shuo Liu, name: Sirui Deng, name: Weiji Zhuang, name: Weiwei Lv, name: Wenyu Yang, name: Xin Zhang, name: Xing Yong, name: Xing Zhang, name: Xingchen Song, name: Xinzhe Xu, name: Xu Wang, name: Yihan Yan, name: Yu Tu, name: Yuanyuan Tian, name: Yudong Wang, name: Yue Yu, name: Zhenru Lin, name: Zhichao Song, name: Zihao Yue
Published: 2025-05-12 14:30:11

We present MiMo-7B, a large language model born for reasoning tasks, with
optimization across both pre-training and post-training stages. During
pre-training, we enhance the data preprocessing pipeline and employ a
three-stage data mixing strategy to strengthen the base model's reasoning
potential. MiMo-7B-Base is pre-trained on 25 trillion tokens, with additional
Multi-Token Prediction objective for enhanced performance and accelerated
inference speed. During post-training, we curate a dataset of 130K verifiable
mathematics and programming problems for reinforcement learning, integrating a
test-difficulty-driven code-reward scheme to alleviate sparse-reward issues and
employing strategic data resampling to stabilize training. Extensive
evaluations show that MiMo-7B-Base possesses exceptional reasoning potential,
outperforming even much larger 32B models. The final RL-tuned model,
MiMo-7B-RL, achieves superior performance on mathematics, code and general
reasoning tasks, surpassing the performance of OpenAI o1-mini. The model
checkpoints are available at https://github.com/xiaomimimo/MiMo.
DanceGRPO: Unleashing GRPO on Visual Generation
Authors: name: Zeyue Xue, name: Jie Wu, name: Yu Gao, name: Fangyuan Kong, name: Lingting Zhu, name: Mengzhao Chen, name: Zhiheng Liu, name: Wei Liu, name: Qiushan Guo, name: Weilin Huang, name: Ping Luo
Published: 2025-05-12 17:59:34

Recent breakthroughs in generative models-particularly diffusion models and
rectified flows-have revolutionized visual content creation, yet aligning model
outputs with human preferences remains a critical challenge. Existing
reinforcement learning (RL)-based methods for visual generation face critical
limitations: incompatibility with modern Ordinary Differential Equations
(ODEs)-based sampling paradigms, instability in large-scale training, and lack
of validation for video generation. This paper introduces DanceGRPO, the first
unified framework to adapt Group Relative Policy Optimization (GRPO) to visual
generation paradigms, unleashing one unified RL algorithm across two generative
paradigms (diffusion models and rectified flows), three tasks (text-to-image,
text-to-video, image-to-video), four foundation models (Stable Diffusion,
HunyuanVideo, FLUX, SkyReel-I2V), and five reward models (image/video
aesthetics, text-image alignment, video motion quality, and binary reward). To
our knowledge, DanceGRPO is the first RL-based unified framework capable of
seamless adaptation across diverse generative paradigms, tasks, foundational
models, and reward models. DanceGRPO demonstrates consistent and substantial
improvements, which outperform baselines by up to 181% on benchmarks such as
HPS-v2.1, CLIP Score, VideoAlign, and GenEval. Notably, DanceGRPO not only can
stabilize policy optimization for complex video generation, but also enables
generative policy to better capture denoising trajectories for Best-of-N
inference scaling and learn from sparse binary feedback. Our results establish
DanceGRPO as a robust and versatile solution for scaling Reinforcement Learning
from Human Feedback (RLHF) tasks in visual generation, offering new insights
into harmonizing reinforcement learning and visual synthesis. The code will be
released.
Learning Dynamics in Continual Pre-Training for Large Language Models
Authors: name: Xingjin Wang, name: Howe Tissue, name: Lu Wang, name: Linjing Li, name: Daniel Dajun Zeng
Published: 2025-05-12 17:47:32

Continual Pre-Training (CPT) has become a popular and effective method to
apply strong foundation models to specific downstream tasks. In this work, we
explore the learning dynamics throughout the CPT process for large language
models. We specifically focus on how general and downstream domain performance
evolves at each training step, with domain performance measured via validation
losses. We have observed that the CPT loss curve fundamentally characterizes
the transition from one curve to another hidden curve, and could be described
by decoupling the effects of distribution shift and learning rate annealing. We
derive a CPT scaling law that combines the two factors, enabling the prediction
of loss at any (continual) training steps and across learning rate schedules
(LRS) in CPT. Our formulation presents a comprehensive understanding of several
critical factors in CPT, including loss potential, peak learning rate, training
steps, replay ratio, etc. Moreover, our approach can be adapted to customize
training hyper-parameters to different CPT goals such as balancing general and
domain-specific performance. Extensive experiments demonstrate that our scaling
law holds across various CPT datasets and training hyper-parameters.
Learning from Peers in Reasoning Models
Authors: name: Tongxu Luo, name: Wenyu Du, name: Jiaxi Bi, name: Stephen Chung, name: Zhengyang Tang, name: Hao Yang, name: Min Zhang, name: Benyou Wang
Published: 2025-05-12 17:39:56

Large Reasoning Models (LRMs) have the ability to self-correct even when they
make mistakes in their reasoning paths. However, our study reveals that when
the reasoning process starts with a short but poor beginning, it becomes
difficult for the model to recover. We refer to this phenomenon as the "Prefix
Dominance Trap". Inspired by psychological findings that peer interaction can
promote self-correction without negatively impacting already accurate
individuals, we propose **Learning from Peers** (LeaP) to address this
phenomenon. Specifically, every tokens, each reasoning path summarizes its
intermediate reasoning and shares it with others through a routing mechanism,
enabling paths to incorporate peer insights during inference. However, we
observe that smaller models sometimes fail to follow summarization and
reflection instructions effectively. To address this, we fine-tune them into
our **LeaP-T** model series. Experiments on AIME 2024, AIME 2025, AIMO 2025,
and GPQA Diamond show that LeaP provides substantial improvements. For
instance, QwQ-32B with LeaP achieves nearly 5 absolute points higher than the
baseline on average, and surpasses DeepSeek-R1-671B on three math benchmarks
with an average gain of 3.3 points. Notably, our fine-tuned LeaP-T-7B matches
the performance of DeepSeek-R1-Distill-Qwen-14B on AIME 2024. In-depth analysis
reveals LeaP's robust error correction by timely peer insights, showing strong
error tolerance and handling varied task difficulty. LeaP marks a milestone by
enabling LRMs to collaborate during reasoning. Our code, datasets, and models
are available at https://learning-from-peers.github.io/ .
REFINE-AF: A Task-Agnostic Framework to Align Language Models via
  Self-Generated Instructions using Reinforcement Learning from Automated
  Feedback
Authors: name: Aniruddha Roy, name: Pretam Ray, name: Abhilash Nandy, name: Somak Aditya, name: Pawan Goyal
Published: 2025-05-10 07:23:19

Instruction-based Large Language Models (LLMs) have proven effective in
numerous few-shot or zero-shot Natural Language Processing (NLP) tasks.
However, creating human-annotated instruction data is time-consuming,
expensive, and often limited in quantity and task diversity. Previous research
endeavors have attempted to address this challenge by proposing frameworks
capable of generating instructions in a semi-automated and task-agnostic manner
directly from the model itself. Many of these efforts have relied on large
API-only parameter-based models such as GPT-3.5 (175B), which are expensive,
and subject to limits on a number of queries. This paper explores the
performance of three open-source small LLMs such as LLaMA 2-7B, LLama 2-13B,
and Mistral 7B, using a semi-automated framework, thereby reducing human
intervention, effort, and cost required to generate an instruction dataset for
fine-tuning LLMs. Furthermore, we demonstrate that incorporating a
Reinforcement Learning (RL) based training algorithm into this LLMs-based
framework leads to further enhancements. Our evaluation of the dataset reveals
that these RL-based frameworks achieve a substantial improvements in 63-66% of
the tasks compared to previous approaches.
WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional
  Websites from Scratch
Authors: name: Zimu Lu, name: Yunqiao Yang, name: Houxing Ren, name: Haotian Hou, name: Han Xiao, name: Ke Wang, name: Weikang Shi, name: Aojun Zhou, name: Mingjie Zhan, name: Hongsheng Li
Published: 2025-05-06 17:59:15

LLM-based agents have demonstrated great potential in generating and managing
code within complex codebases. In this paper, we introduce WebGen-Bench, a
novel benchmark designed to measure an LLM-based agent's ability to create
multi-file website codebases from scratch. It contains diverse instructions for
website generation, created through the combined efforts of human annotators
and GPT-4o. These instructions span three major categories and thirteen minor
categories, encompassing nearly all important types of web applications. To
assess the quality of the generated websites, we use GPT-4o to generate test
cases targeting each functionality described in the instructions, and then
manually filter, adjust, and organize them to ensure accuracy, resulting in 647
test cases. Each test case specifies an operation to be performed on the
website and the expected result after the operation. To automate testing and
improve reproducibility, we employ a powerful web-navigation agent to execute
tests on the generated websites and determine whether the observed responses
align with the expected results. We evaluate three high-performance code-agent
frameworks, Bolt.diy, OpenHands, and Aider, using multiple proprietary and
open-source LLMs as engines. The best-performing combination, Bolt.diy powered
by DeepSeek-R1, achieves only 27.8\% accuracy on the test cases, highlighting
the challenging nature of our benchmark. Additionally, we construct
WebGen-Instruct, a training set consisting of 6,667 website-generation
instructions. Training Qwen2.5-Coder-32B-Instruct on Bolt.diy trajectories
generated from a subset of this training set achieves an accuracy of 38.2\%,
surpassing the performance of the best proprietary model.
Multiview Point Cloud Registration via Optimization in an Autoencoder
  Latent Space
Authors: name: Luc Vedrenne, name: Sylvain Faisan, name: Denis Fortun
Published: 2025-04-30 09:42:38

Point cloud rigid registration is a fundamental problem in 3D computer
vision. In the multiview case, we aim to find a set of 6D poses to align a set
of objects. Methods based on pairwise registration rely on a subsequent
synchronization algorithm, which makes them poorly scalable with the number of
views. Generative approaches overcome this limitation, but are based on
Gaussian Mixture Models and use an Expectation-Maximization algorithm. Hence,
they are not well suited to handle large transformations. Moreover, most
existing methods cannot handle high levels of degradations. In this paper, we
introduce POLAR (POint cloud LAtent Registration), a multiview registration
method able to efficiently deal with a large number of views, while being
robust to a high level of degradations and large initial angles. To achieve
this, we transpose the registration problem into the latent space of a
pretrained autoencoder, design a loss taking degradations into account, and
develop an efficient multistart optimization strategy. Our proposed method
significantly outperforms state-of-the-art approaches on synthetic and real
data. POLAR is available at github.com/pypolar/polar or as a standalone package
which can be installed with pip install polaregistration.
A Preliminary Study for GPT-4o on Image Restoration
Authors: name: Hao Yang, name: Yan Yang, name: Ruikun Zhang, name: Liyuan Pan
Published: 2025-05-08 20:00:11

OpenAI's GPT-4o model, integrating multi-modal inputs and outputs within an
autoregressive architecture, has demonstrated unprecedented performance in
image generation. In this work, we investigate its potential impact on the
image restoration community. We present the first systematic evaluation of
GPT-4o across diverse restoration tasks. Our experiments reveal that, although
restoration outputs from GPT-4o are visually appealing, they often suffer from
pixel-level structural fidelity when compared to ground-truth images. Common
issues are variations in image proportions, shifts in object positions and
quantities, and changes in viewpoint.To address it, taking image dehazing,
derainning, and low-light enhancement as representative case studies, we show
that GPT-4o's outputs can serve as powerful visual priors, substantially
enhancing the performance of existing dehazing networks. It offers practical
guidelines and a baseline framework to facilitate the integration of GPT-4o
into future image restoration pipelines. We hope the study on GPT-4o image
restoration will accelerate innovation in the broader field of image generation
areas. To support further research, we will release GPT-4o-restored images from
over 10 widely used image restoration datasets.