Orionnnn/hub-stats huggingface dataset:
Training-Free Watermarking for Autoregressive Image Generation
Authors: name: Yu Tong, name: Zihao Pan, name: Shuai Yang, name: Kaiyang Zhou
Published: 2025-05-20 17:58:02

Invisible image watermarking can protect image ownership and prevent
malicious misuse of visual generative models. However, existing generative
watermarking methods are mainly designed for diffusion models while
watermarking for autoregressive image generation models remains largely
underexplored. We propose IndexMark, a training-free watermarking framework for
autoregressive image generation models. IndexMark is inspired by the redundancy
property of the codebook: replacing autoregressively generated indices with
similar indices produces negligible visual differences. The core component in
IndexMark is a simple yet effective match-then-replace method, which carefully
selects watermark tokens from the codebook based on token similarity, and
promotes the use of watermark tokens through token replacement, thereby
embedding the watermark without affecting the image quality. Watermark
verification is achieved by calculating the proportion of watermark tokens in
generated images, with precision further improved by an Index Encoder.
Furthermore, we introduce an auxiliary validation scheme to enhance robustness
against cropping attacks. Experiments demonstrate that IndexMark achieves
state-of-the-art performance in terms of image quality and verification
accuracy, and exhibits robustness against various perturbations, including
cropping, noises, Gaussian blur, random erasing, color jittering, and JPEG
compression.
Fine-tuning Quantized Neural Networks with Zeroth-order Optimization
Authors: name: Sifeng Shang, name: Jiayi Zhou, name: Chenyu Lin, name: Minxian Li, name: Kaiyang Zhou
Published: 2025-05-19 17:55:15

As the size of large language models grows exponentially, GPU memory has
become a bottleneck for adapting these models to downstream tasks. In this
paper, we aim to push the limits of memory-efficient training by minimizing
memory usage on model weights, gradients, and optimizer states, within a
unified framework. Our idea is to eliminate both gradients and optimizer states
using zeroth-order optimization, which approximates gradients by perturbing
weights during forward passes to identify gradient directions. To minimize
memory usage on weights, we employ model quantization, e.g., converting from
bfloat16 to int4. However, directly applying zeroth-order optimization to
quantized weights is infeasible due to the precision gap between discrete
weights and continuous gradients, which would otherwise require de-quantization
and re-quantization. To overcome this challenge, we propose Quantized
Zeroth-order Optimization (QZO), a novel approach that perturbs the continuous
quantization scale for gradient estimation and uses a directional derivative
clipping method to stabilize training. QZO is orthogonal to both scalar-based
and codebook-based post-training quantization methods. Compared to
full-parameter fine-tuning in bfloat16, QZO can reduce the total memory cost by
more than 18times for 4-bit LLMs, and enables fine-tuning Llama-2-13B and
Stable Diffusion 3.5 Large within a single 24GB GPU.
Warm Up Before You Train: Unlocking General Reasoning in
  Resource-Constrained Settings
Authors: name: Safal Shrestha, name: Minwu Kim, name: Aadim Nepal, name: Anubhav Shrestha, name: Keith Ross
Published: 2025-05-19 20:29:15

Designing effective reasoning-capable LLMs typically requires training using
Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with
carefully curated Long Chain of Thoughts (CoT), both of which depend heavily on
extensive training data. This creates a major challenge when the amount of
quality training data is scarce. We propose a sample-efficient, two-stage
training strategy to develop reasoning LLMs under limited supervision. In the
first stage, we "warm up" the model by distilling Long CoTs from a toy domain,
namely, Knights \& Knaves (K\&K) logic puzzles to acquire general reasoning
skills. In the second stage, we apply RLVR to the warmed-up model using a
limited set of target-domain examples. Our experiments demonstrate that this
two-phase approach offers several benefits: (i) the warmup phase alone
facilitates generalized reasoning, leading to performance improvements across a
range of tasks, including MATH, HumanEval^{+}, and MMLU-Pro. (ii) When both
the base model and the warmed-up model are RLVR trained on the same small
dataset (leq100 examples), the warmed-up model consistently outperforms the
base model; (iii) Warming up before RLVR training allows a model to maintain
cross-domain generalizability even after training on a specific domain; (iv)
Introducing warmup in the pipeline improves not only accuracy but also overall
sample efficiency during RLVR training. The results in this paper highlight the
promise of warmup for building robust reasoning LLMs in data-scarce
environments.
Exploring Federated Pruning for Large Language Models
Authors: name: Pengxin Guo, name: Yinong Wang, name: Wei Li, name: Mengting Liu, name: Ming Li, name: Jinkai Zheng, name: Liangqiong Qu
Published: 2025-05-19 03:41:54

LLM pruning has emerged as a promising technology for compressing LLMs,
enabling their deployment on resource-limited devices. However, current
methodologies typically require access to public calibration samples, which can
be challenging to obtain in privacy-sensitive domains. To address this issue,
we introduce FedPrLLM, a comprehensive federated pruning framework designed for
the privacy-preserving compression of LLMs. In FedPrLLM, each client only needs
to calculate a pruning mask matrix based on its local calibration data and
share it with the server to prune the global model. This approach allows for
collaborative pruning of the global model with the knowledge of each client
while maintaining local data privacy. Additionally, we conduct extensive
experiments to explore various possibilities within the FedPrLLM framework,
including different comparison groups, pruning strategies, and the decision to
scale weights. Our extensive evaluation reveals that one-shot pruning with
layer comparison and no weight scaling is the optimal choice within the
FedPrLLM framework. We hope our work will help guide future efforts in pruning
LLMs in privacy-sensitive fields. Our code is available at
https://github.com/Pengxin-Guo/FedPrLLM.
Think Only When You Need with Large Hybrid-Reasoning Models
Authors: name: Lingjie Jiang, name: Xun Wu, name: Shaohan Huang, name: Qingxiu Dong, name: Zewen Chi, name: Li Dong, name: Xingxing Zhang, name: Tengchao Lv, name: Lei Cui, name: Furu Wei
Published: 2025-05-20 17:23:25

Recent Large Reasoning Models (LRMs) have shown substantially improved
reasoning capabilities over traditional Large Language Models (LLMs) by
incorporating extended thinking processes prior to producing final responses.
However, excessively lengthy thinking introduces substantial overhead in terms
of token consumption and latency, which is particularly unnecessary for simple
queries. In this work, we introduce Large Hybrid-Reasoning Models (LHRMs), the
first kind of model capable of adaptively determining whether to perform
thinking based on the contextual information of user queries. To achieve this,
we propose a two-stage training pipeline comprising Hybrid Fine-Tuning (HFT) as
a cold start, followed by online reinforcement learning with the proposed
Hybrid Group Policy Optimization (HGPO) to implicitly learn to select the
appropriate thinking mode. Furthermore, we introduce a metric called Hybrid
Accuracy to quantitatively assess the model's capability for hybrid thinking.
Extensive experimental results show that LHRMs can adaptively perform hybrid
thinking on queries of varying difficulty and type. It outperforms existing
LRMs and LLMs in reasoning and general capabilities while significantly
improving efficiency. Together, our work advocates for a reconsideration of the
appropriate use of extended thinking processes and provides a solid starting
point for building hybrid thinking systems.
MIGRATION-BENCH: Repository-Level Code Migration Benchmark from Java 8
Authors: name: Linbo Liu, name: Xinle Liu, name: Qiang Zhou, name: Lin Chen, name: Yihan Liu, name: Hoan Nguyen, name: Behrooz Omidvar-Tehrani, name: Xi Shen, name: Jun Huan, name: Omer Tripp, name: Anoop Deoras
Published: 2025-05-14 17:11:23

With the rapid advancement of powerful large language models (LLMs) in recent
years, a wide range of software engineering tasks can now be addressed using
LLMs, significantly enhancing productivity and scalability. Numerous benchmark
datasets have been developed to evaluate the coding capabilities of these
models, while they primarily focus on problem-solving and issue-resolution
tasks. In contrast, we introduce a new coding benchmark MIGRATION-BENCH with a
distinct focus: code migration. MIGRATION-BENCH aims to serve as a
comprehensive benchmark for migration from Java 8 to the latest long-term
support (LTS) versions (Java 17, 21), MIGRATION-BENCH includes a full dataset
and its subset selected with 5,102 and 300 repositories respectively.
Selected is a representative subset curated for complexity and difficulty,
offering a versatile resource to support research in the field of code
migration. Additionally, we provide a comprehensive evaluation framework to
facilitate rigorous and standardized assessment of LLMs on this challenging
task. We further propose SD-Feedback and demonstrate that LLMs can effectively
tackle repository-level code migration to Java 17. For the selected subset with
Claude-3.5-Sonnet-v2, SD-Feedback achieves 62.33% and 27.00% success rate
(pass@1) for minimal and maximal migration respectively. The benchmark dataset
and source code are available at:
https://huggingface.co/collections/AmazonScience and
https://github.com/amazon-science/self_debug respectively.
Reasoning Models Better Express Their Confidence
Authors: name: Dongkeun Yoon, name: Seungone Kim, name: Sohee Yang, name: Sunkyoung Kim, name: Soyeon Kim, name: Yongil Kim, name: Eunbi Choi, name: Yireun Kim, name: Minjoon Seo
Published: 2025-05-20 15:19:00

Despite their strengths, large language models (LLMs) often fail to
communicate their confidence accurately, making it difficult to assess when
they might be wrong and limiting their reliability. In this work, we
demonstrate that reasoning models-LLMs that engage in extended chain-of-thought
(CoT) reasoning-exhibit superior performance not only in problem-solving but
also in accurately expressing their confidence. Specifically, we benchmark six
reasoning models across six datasets and find that they achieve strictly better
confidence calibration than their non-reasoning counterparts in 33 out of the
36 settings. Our detailed analysis reveals that these gains in calibration stem
from the slow thinking behaviors of reasoning models-such as exploring
alternative approaches and backtracking-which enable them to adjust their
confidence dynamically throughout their CoT, making it progressively more
accurate. In particular, we find that reasoning models become increasingly
better calibrated as their CoT unfolds, a trend not observed in non-reasoning
models. Moreover, removing slow thinking behaviors from the CoT leads to a
significant drop in calibration. Lastly, we show that these gains are not
exclusive to reasoning models-non-reasoning models also benefit when guided to
perform slow thinking via in-context learning.
Reward Reasoning Model
Authors: name: Jiaxin Guo, name: Zewen Chi, name: Li Dong, name: Qingxiu Dong, name: Xun Wu, name: Shaohan Huang, name: Furu Wei
Published: 2025-05-20 17:58:03

Reward models play a critical role in guiding large language models toward
outputs that align with human expectations. However, an open challenge remains
in effectively utilizing test-time compute to enhance reward model performance.
In this work, we introduce Reward Reasoning Models (RRMs), which are
specifically designed to execute a deliberate reasoning process before
generating final rewards. Through chain-of-thought reasoning, RRMs leverage
additional test-time compute for complex queries where appropriate rewards are
not immediately apparent. To develop RRMs, we implement a reinforcement
learning framework that fosters self-evolved reward reasoning capabilities
without requiring explicit reasoning traces as training data. Experimental
results demonstrate that RRMs achieve superior performance on reward modeling
benchmarks across diverse domains. Notably, we show that RRMs can adaptively
exploit test-time compute to further improve reward accuracy. The pretrained
reward reasoning models are available at
https://huggingface.co/Reward-Reasoning.
CS-Sum: A Benchmark for Code-Switching Dialogue Summarization and the
  Limits of Large Language Models
Authors: name: Sathya Krishnan Suresh, name: Tanmay Surana, name: Lim Zhi Hao, name: Eng Siong Chng
Published: 2025-05-19 09:18:14

Code-switching (CS) poses a significant challenge for Large Language Models
(LLMs), yet its comprehensibility remains underexplored in LLMs. We introduce
CS-Sum, to evaluate the comprehensibility of CS by the LLMs through CS dialogue
to English summarization. CS-Sum is the first benchmark for CS dialogue
summarization across Mandarin-English (EN-ZH), Tamil-English (EN-TA), and
Malay-English (EN-MS), with 900-1300 human-annotated dialogues per language
pair. Evaluating ten LLMs, including open and closed-source models, we analyze
performance across few-shot, translate-summarize, and fine-tuning (LoRA, QLoRA
on synthetic data) approaches. Our findings show that though the scores on
automated metrics are high, LLMs make subtle mistakes that alter the complete
meaning of the dialogue. To this end, we introduce 3 most common type of errors
that LLMs make when handling CS input. Error rates vary across CS pairs and
LLMs, with some LLMs showing more frequent errors on certain language pairs,
underscoring the need for specialized training on code-switched data.
VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation
Authors: name: Wentao Ma, name: Weiming Ren, name: Yiming Jia, name: Zhuofeng Li, name: Ping Nie, name: Ge Zhang, name: Wenhu Chen
Published: 2025-05-20 17:26:32

Large multimodal models (LMMs) have recently emerged as a powerful tool for
long video understanding (LVU), prompting the development of standardized LVU
benchmarks to evaluate their performance. However, our investigation reveals a
rather sober lesson for existing LVU benchmarks. First, most existing
benchmarks rely heavily on multiple-choice questions (MCQs), whose evaluation
results are inflated due to the possibility of guessing the correct answer;
Second, a significant portion of questions in these benchmarks have strong
priors to allow models to answer directly without even reading the input video.
For example, Gemini-1.5-Pro can achieve over 50\% accuracy given a random frame
from a long video on Video-MME. We also observe that increasing the number of
frames does not necessarily lead to improvement on existing benchmarks, which
is counterintuitive. As a result, the validity and robustness of current LVU
benchmarks are undermined, impeding a faithful assessment of LMMs' long-video
understanding capability. To tackle this problem, we propose VideoEval-Pro, a
realistic LVU benchmark containing questions with open-ended short-answer,
which truly require understanding the entire video. VideoEval-Pro assesses both
segment-level and full-video understanding through perception and reasoning
tasks. By evaluating 21 proprietary and open-source video LMMs, we conclude the
following findings: (1) video LMMs show drastic performance (>25\%) drops on
open-ended questions compared with MCQs; (2) surprisingly, higher MCQ scores do
not lead to higher open-ended scores on VideoEval-Pro; (3) compared to other
MCQ benchmarks, VideoEval-Pro benefits more from increasing the number of input
frames. Our results show that VideoEval-Pro offers a more realistic and
reliable measure of long video understanding, providing a clearer view of
progress in this domain.
General-Reasoner: Advancing LLM Reasoning Across All Domains
Authors: name: Xueguang Ma, name: Qian Liu, name: Dongfu Jiang, name: Ge Zhang, name: Zejun Ma, name: Wenhu Chen
Published: 2025-05-20 17:41:33

Reinforcement learning (RL) has recently demonstrated strong potential in
enhancing the reasoning capabilities of large language models (LLMs).
Particularly, the "Zero" reinforcement learning introduced by Deepseek-R1-Zero,
enables direct RL training of base LLMs without relying on an intermediate
supervised fine-tuning stage. Despite these advancements, current works for LLM
reasoning mainly focus on mathematical and coding domains, largely due to data
abundance and the ease of answer verification. This limits the applicability
and generalization of such models to broader domains, where questions often
have diverse answer representations, and data is more scarce. In this paper, we
propose General-Reasoner, a novel training paradigm designed to enhance LLM
reasoning capabilities across diverse domains. Our key contributions include:
(1) constructing a large-scale, high-quality dataset of questions with
verifiable answers curated by web crawling, covering a wide range of
disciplines; and (2) developing a generative model-based answer verifier, which
replaces traditional rule-based verification with the capability of
chain-of-thought and context-awareness. We train a series of models and
evaluate them on a wide range of datasets covering wide domains like physics,
chemistry, finance, electronics etc. Our comprehensive evaluation across these
12 benchmarks (e.g. MMLU-Pro, GPQA, SuperGPQA, TheoremQA, BBEH and MATH AMC)
demonstrates that General-Reasoner outperforms existing baseline methods,
achieving robust and generalizable reasoning performance while maintaining
superior effectiveness in mathematical reasoning tasks.
SSR: Enhancing Depth Perception in Vision-Language Models via
  Rationale-Guided Spatial Reasoning
Authors: name: Yang Liu, name: Ming Ma, name: Xiaomin Yu, name: Pengxiang Ding, name: Han Zhao, name: Mingyang Sun, name: Siteng Huang, name: Donglin Wang
Published: 2025-05-18 14:40:16

Despite impressive advancements in Visual-Language Models (VLMs) for
multi-modal tasks, their reliance on RGB inputs limits precise spatial
understanding. Existing methods for integrating spatial cues, such as point
clouds or depth, either require specialized sensors or fail to effectively
exploit depth information for higher-order reasoning. To this end, we propose a
novel Spatial Sense and Reasoning method, dubbed SSR, a novel framework that
transforms raw depth data into structured, interpretable textual rationales.
These textual rationales serve as meaningful intermediate representations to
significantly enhance spatial reasoning capabilities. Additionally, we leverage
knowledge distillation to compress the generated rationales into compact latent
embeddings, which facilitate resource-efficient and plug-and-play integration
into existing VLMs without retraining. To enable comprehensive evaluation, we
introduce a new dataset named SSR-CoT, a million-scale visual-language
reasoning dataset enriched with intermediate spatial reasoning annotations, and
present SSRBench, a comprehensive multi-task benchmark. Extensive experiments
on multiple benchmarks demonstrate SSR substantially improves depth utilization
and enhances spatial reasoning, thereby advancing VLMs toward more human-like
multi-modal understanding. Our project page is at
https://yliu-cs.github.io/SSR.
Not All Correct Answers Are Equal: Why Your Distillation Source Matters
Authors: name: Xiaoyu Tian, name: Yunjie Ji, name: Haotian Wang, name: Shuaiting Chen, name: Sitong Zhao, name: Yiping Peng, name: Han Zhao, name: Xiangang Li
Published: 2025-05-20 15:00:51

Distillation has emerged as a practical and effective approach to enhance the
reasoning capabilities of open-source language models. In this work, we conduct
a large-scale empirical study on reasoning data distillation by collecting
verified outputs from three state-of-the-art teacher models-AM-Thinking-v1,
Qwen3-235B-A22B, and DeepSeek-R1-on a shared corpus of 1.89 million queries. We
construct three parallel datasets and analyze their distributions, revealing
that AM-Thinking-v1-distilled data exhibits greater token length diversity and
lower perplexity. Student models trained on each dataset are evaluated on
reasoning benchmarks including AIME2024, AIME2025, MATH500, and LiveCodeBench.
The AM-based model consistently achieves the best performance (e.g., 84.3 on
AIME2024, 72.2 on AIME2025, 98.4 on MATH500, and 65.9 on LiveCodeBench) and
demonstrates adaptive output behavior-producing longer responses for harder
tasks and shorter ones for simpler tasks. These findings highlight the value of
high-quality, verified reasoning traces. We release the AM-Thinking-v1 and
Qwen3-235B-A22B distilled datasets to support future research on open and
high-performing reasoning-oriented language models. The datasets are publicly
available on Hugging FaceDatasets are available on Hugging Face:
\href{https://huggingface.co/datasets/a-m-team/AM-Thinking-v1-Distilled{AM-Thinking-v1-Distilled},
https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled{AM-Qwen3-Distilled}.}.
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model
Authors: name: Ruihuang Li, name: Caijin Zhou, name: Shoujian Zheng, name: Jianxiang Lu, name: Jiabin Huang, name: Comi Chen, name: Junshu Tang, name: Guangzheng Xu, name: Jiale Tao, name: Hongmei Wang, name: Donghao Li, name: Wenqing Yu, name: Senbo Wang, name: Zhimin Li, name: Yetshuan Shi, name: Haoyu Yang, name: Yukun Wang, name: Wenxun Dai, name: Jiaqi Li, name: Linqing Wang, name: Qixun Wang, name: Zhiyong Xu, name: Yingfang Zhang, name: Jiangfeng Xiong, name: Weijie Kong, name: Chao Zhang, name: Hongxin Zhang, name: Qiaoling Zheng, name: Weiting Guo, name: Xinchi Deng, name: Yixuan Li, name: Renjia Wei, name: Yulin Jian, name: Duojun Huang, name: Xuhua Ren, name: Sihuan Lin, name: Yifu Sun, name: Yuan Zhou, name: Joey Wang, name: Qin Lin, name: Jingmiao Yu, name: Jihong Zhang, name: Caesar Zhong, name: Di Wang, name: Yuhong Liu, name: Linus, name: Jie Jiang, name: Longhuang Wu, name: Shuai Shao, name: Qinglin Lu
Published: 2025-05-20 09:39:48

Intelligent game creation represents a transformative advancement in game
development, utilizing generative artificial intelligence to dynamically
generate and enhance game content. Despite notable progress in generative
models, the comprehensive synthesis of high-quality game assets, including both
images and videos, remains a challenging frontier. To create high-fidelity game
content that simultaneously aligns with player preferences and significantly
boosts designer efficiency, we present Hunyuan-Game, an innovative project
designed to revolutionize intelligent game production. Hunyuan-Game encompasses
two primary branches: image generation and video generation. The image
generation component is built upon a vast dataset comprising billions of game
images, leading to the development of a group of customized image generation
models tailored for game scenarios: (1) General Text-to-Image Generation. (2)
Game Visual Effects Generation, involving text-to-effect and reference
image-based game visual effect generation. (3) Transparent Image Generation for
characters, scenes, and game visual effects. (4) Game Character Generation
based on sketches, black-and-white images, and white models. The video
generation component is built upon a comprehensive dataset of millions of game
and anime videos, leading to the development of five core algorithmic models,
each targeting critical pain points in game development and having robust
adaptation to diverse game video scenarios: (1) Image-to-Video Generation. (2)
360 A/T Pose Avatar Video Synthesis. (3) Dynamic Illustration Generation. (4)
Generative Video Super-Resolution. (5) Interactive Game Video Generation. These
image and video generation models not only exhibit high-level aesthetic
expression but also deeply integrate domain-specific knowledge, establishing a
systematic understanding of diverse game and anime art styles.