Orionnnn/hub-stats huggingface dataset:
GuardReasoner: Towards Reasoning-based LLM Safeguards
Authors: name: Yue Liu, name: Hongcheng Gao, name: Shengfang Zhai, name: Jun Xia, name: Tianyi Wu, name: Zhiwei Xue, name: Yulin Chen, name: Kenji Kawaguchi, name: Jiaheng Zhang, name: Bryan Hooi
Published: 2025-01-30 17:06:06

As LLMs increasingly impact safety-critical applications, ensuring their
safety using guardrails remains a key challenge. This paper proposes
GuardReasoner, a new safeguard for LLMs, by guiding the guard model to learn to
reason. Concretely, we first create the GuardReasonerTrain dataset, which
consists of 127K samples with 460K detailed reasoning steps. Then, we introduce
reasoning SFT to unlock the reasoning capability of guard models. In addition,
we present hard sample DPO to further strengthen their reasoning ability. In
this manner, GuardReasoner achieves better performance, explainability, and
generalizability. Extensive experiments and analyses on 13 benchmarks of 3
guardrail tasks demonstrate its superiority. Remarkably, GuardReasoner 8B
surpasses GPT-4o+CoT by 5.74% and LLaMA Guard 3 8B by 20.84% F1 score on
average. We release the training data, code, and models with different scales
(1B, 3B, 8B) of GuardReasoner : https://github.com/yueliu1999/GuardReasoner/.
Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
  Virtual Clothing Tasks
Authors: name: Hailong Guo, name: Bohan Zeng, name: Yiren Song, name: Wentao Zhang, name: Chuang Zhang, name: Jiaming Liu
Published: 2025-01-27 09:33:23

Image-based virtual try-on (VTON) aims to generate a virtual try-on result by
transferring an input garment onto a target person's image. However, the
scarcity of paired garment-model data makes it challenging for existing methods
to achieve high generalization and quality in VTON. Also, it limits the ability
to generate mask-free try-ons. To tackle the data scarcity problem, approaches
such as Stable Garment and MMTryon use a synthetic data strategy, effectively
increasing the amount of paired data on the model side. However, existing
methods are typically limited to performing specific try-on tasks and lack
user-friendliness. To enhance the generalization and controllability of VTON
generation, we propose Any2AnyTryon, which can generate try-on results based on
different textual instructions and model garment images to meet various needs,
eliminating the reliance on masks, poses, or other conditions. Specifically, we
first construct the virtual try-on dataset LAION-Garment, the largest known
open-source garment try-on dataset. Then, we introduce adaptive position
embedding, which enables the model to generate satisfactory outfitted model
images or garment images based on input images of different sizes and
categories, significantly enhancing the generalization and controllability of
VTON generation. In our experiments, we demonstrate the effectiveness of our
Any2AnyTryon and compare it with existing methods. The results show that
Any2AnyTryon enables flexible, controllable, and high-quality image-based
virtual try-on generation.https://logn-2024.github.io/Any2anyTryonProjectPage/
People who frequently use ChatGPT for writing tasks are accurate and
  robust detectors of AI-generated text
Authors: name: Jenna Russell, name: Marzena Karpinska, name: Mohit Iyyer
Published: 2025-01-26 19:31:34

In this paper, we study how well humans can detect text generated by
commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300
non-fiction English articles, label them as either human-written or
AI-generated, and provide paragraph-length explanations for their decisions.
Our experiments show that annotators who frequently use LLMs for writing tasks
excel at detecting AI-generated text, even without any specialized training or
feedback. In fact, the majority vote among five such "expert" annotators
misclassifies only 1 of 300 articles, significantly outperforming most
commercial and open-source detectors we evaluated even in the presence of
evasion tactics like paraphrasing and humanization. Qualitative analysis of the
experts' free-form explanations shows that while they rely heavily on specific
lexical clues ('AI vocabulary'), they also pick up on more complex phenomena
within the text (e.g., formality, originality, clarity) that are challenging to
assess for automatic detectors. We release our annotated dataset and code to
spur future research into both human and automated detection of AI-generated
text.
Exploring the sustainable scaling of AI dilemma: A projective study of
  corporations' AI environmental impacts
Authors: name: Clément Desroches, name: Martin Chauvin, name: Louis Ladan, name: Caroline Vateau, name: Simon Gosset, name: Philippe Cordier
Published: 2025-01-24 08:58:49

The rapid growth of artificial intelligence (AI), particularly Large Language
Models (LLMs), has raised concerns regarding its global environmental impact
that extends beyond greenhouse gas emissions to include consideration of
hardware fabrication and end-of-life processes. The opacity from major
providers hinders companies' abilities to evaluate their AI-related
environmental impacts and achieve net-zero targets.
  In this paper, we propose a methodology to estimate the environmental impact
of a company's AI portfolio, providing actionable insights without
necessitating extensive AI and Life-Cycle Assessment (LCA) expertise. Results
confirm that large generative AI models consume up to 4600x more energy than
traditional models. Our modelling approach, which accounts for increased AI
usage, hardware computing efficiency, and changes in electricity mix in line
with IPCC scenarios, forecasts AI electricity use up to 2030. Under a high
adoption scenario, driven by widespread Generative AI and agents adoption
associated to increasingly complex models and frameworks, AI electricity use is
projected to rise by a factor of 24.4.
  Mitigating the environmental impact of Generative AI by 2030 requires
coordinated efforts across the AI value chain. Isolated measures in hardware
efficiency, model efficiency, or grid improvements alone are insufficient. We
advocate for standardized environmental assessment frameworks, greater
transparency from the all actors of the value chain and the introduction of a
"Return on Environment" metric to align AI development with net-zero goals.
Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing
  Guardrail Moderation
Authors: name: Tiansheng Huang, name: Sihao Hu, name: Fatih Ilhan, name: Selim Furkan Tekin, name: Ling Liu
Published: 2025-01-29 06:24:58

Recent research shows that Large Language Models (LLMs) are vulnerable to
harmful fine-tuning attacks -- models lose their safety alignment ability after
fine-tuning on a few harmful samples. For risk mitigation, a guardrail is
typically used to filter out harmful samples before fine-tuning. By designing a
new red-teaming method, we in this paper show that purely relying on the
moderation guardrail for data filtration is not reliable. Our proposed attack
method, dubbed Virus, easily bypasses the guardrail moderation by slightly
modifying the harmful data. Experimental results show that the harmful data
optimized by Virus is not detectable by the guardrail with up to 100\% leakage
ratio, and can simultaneously achieve superior attack performance. Finally, the
key message we want to convey through this paper is that: it is
reckless to consider guardrail moderation as a clutch at straws towards harmful
fine-tuning attack, as it cannot solve the inherent safety issue of the
pre-trained LLMs. Our code is available at https://github.com/git-disl/Virus
Critique Fine-Tuning: Learning to Critique is More Effective than
  Learning to Imitate
Authors: name: Yubo Wang, name: Xiang Yue, name: Wenhu Chen
Published: 2025-01-29 15:20:30

Supervised Fine-Tuning (SFT) is commonly used to train language models to
imitate annotated responses for given instructions. In this paper, we challenge
this paradigm and propose Critique Fine-Tuning (CFT), a strategy where models
learn to critique noisy responses rather than simply imitate correct ones.
Inspired by human learning processes that emphasize critical thinking, CFT
encourages deeper analysis and nuanced understanding-traits often overlooked by
standard SFT. To validate the effectiveness of CFT, we construct a 50K-sample
dataset from WebInstruct, using GPT-4o as the teacher to generate critiques in
the form of (input=[query; noisy response], output=critique). CFT on this
dataset yields a consistent 4-10% improvement over SFT on six math benchmarks
with different base models like Qwen2.5, Qwen2.5-Math and DeepSeek-Math. We
further expand to MetaMath and NuminaMath datasets and observe similar gains
over SFT. Notably, our Qwen2.5-Math-CFT model-trained on just 50K
samples-matches or outperforms competitive models such as AceMath and
Qwen2.5-Math-Instruct on most benchmarks, both of which use over 2M samples.
Ablation studies show that CFT is robust to the source of noisy response and
teacher critique model. Through these findings, we argue that critique-based
training offers a more effective alternative to advance the reasoning of
language models.
Atla Selene Mini: A General Purpose Evaluation Model
Authors: name: Andrei Alexandru, name: Antonia Calvi, name: Henry Broomfield, name: Jackson Golden, name: Kyle Dai, name: Mathias Leys, name: Maurice Burger, name: Max Bartolo, name: Roman Engeler, name: Sashank Pisupati, name: Toby Drane, name: Young Sun Park
Published: 2025-01-27 15:09:08

We introduce Atla Selene Mini, a state-of-the-art small language
model-as-a-judge (SLMJ). Selene Mini is a general-purpose evaluator that
outperforms the best SLMJs and GPT-4o-mini on overall performance across 11
out-of-distribution benchmarks, spanning absolute scoring, classification, and
pairwise preference tasks. It is the highest-scoring 8B generative model on
RewardBench, surpassing strong baselines like GPT-4o and specialized judges. To
achieve this, we develop a principled data curation strategy that augments
public datasets with synthetically generated critiques and ensures high quality
through filtering and dataset ablations. We train our model on a combined
direct preference optimization (DPO) and supervised fine-tuning (SFT) loss, and
produce a highly promptable evaluator that excels in real-world scenarios.
Selene Mini shows dramatically improved zero-shot agreement with human expert
evaluations on financial and medical industry datasets. It is also robust to
variations in prompt format. Preliminary results indicate that Selene Mini is
the top-ranking evaluator in a live, community-driven Judge Arena. We release
the model weights on HuggingFace
(https://hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B) and Ollama to encourage
widespread community adoption.
Early External Safety Testing of OpenAI's o3-mini: Insights from the
  Pre-Deployment Evaluation
Authors: name: Aitor Arrieta, name: Miriam Ugarte, name: Pablo Valle, name: José Antonio Parejo, name: Sergio Segura
Published: 2025-01-29 16:36:53

Large Language Models (LLMs) have become an integral part of our daily lives.
However, they impose certain risks, including those that can harm individuals'
privacy, perpetuate biases and spread misinformation. These risks highlight the
need for robust safety mechanisms, ethical guidelines, and thorough testing to
ensure their responsible deployment. Safety of LLMs is a key property that
needs to be thoroughly tested prior the model to be deployed and accessible to
the general users. This paper reports the external safety testing experience
conducted by researchers from Mondragon University and University of Seville on
OpenAI's new o3-mini LLM as part of OpenAI's early access for safety testing
program. In particular, we apply our tool, ASTRAL, to automatically and
systematically generate up to date unsafe test inputs (i.e., prompts) that
helps us test and assess different safety categories of LLMs. We automatically
generate and execute a total of 10,080 unsafe test input on a early o3-mini
beta version. After manually verifying the test cases classified as unsafe by
ASTRAL, we identify a total of 87 actual instances of unsafe LLM behavior. We
highlight key insights and findings uncovered during the pre-deployment
external testing phase of OpenAI's latest LLM.
DeepFlow: Serverless Large Language Model Serving at Scale
Authors: name: Junhao Hu, name: Jiang Xu, name: Zhixia Liu, name: Yulong He, name: Yuetao Chen, name: Hao Xu, name: Jiang Liu, name: Baoquan Zhang, name: Shining Wan, name: Gengyuan Dan, name: Zhiyu Dong, name: Zhihao Ren, name: Jie Meng, name: Chao He, name: Changhong Liu, name: Tao Xie, name: Dayun Lin, name: Qin Zhang, name: Yue Yu, name: Hao Feng, name: Xusheng Chen, name: Yizhou Shan
Published: 2025-01-24 11:34:13

This paper introduces DeepFlow, a scalable and serverless AI platform
designed to efficiently serve large language models (LLMs) at scale in cloud
environments. DeepFlow addresses key challenges such as resource allocation,
serving efficiency, and cold start latencies through four main design
components. First, it uses a simple serverless abstraction called the
request-job-task model, which helps manage AI workloads across post-training
and model serving tasks. Second, it builds an in-house serving engine FlowServe
using a microkernel-inspired design, NPU-centric execution, and SPMD-based
parallelism to optimize LLM serving. The system also includes novel scheduling
policies tailored for both PD-disaggregated and PD-colocated configurations.
With optimizations like pre-warmed pods, DRAM pre-loading, and NPU-fork,
DeepFlow can scale up to 64 instances in seconds. DeepFlow has been in
production for over a year, operating on a large Ascend NPU cluster and
providing industrystandard APIs for fine-tuning, agent serving, and model
serving to our customers.
TAID: Temporally Adaptive Interpolated Distillation for Efficient
  Knowledge Transfer in Language Models
Authors: name: Makoto Shing, name: Kou Misaki, name: Han Bao, name: Sho Yokoi, name: Takuya Akiba
Published: 2025-01-28 13:31:18

Causal language models have demonstrated remarkable capabilities, but their
size poses significant challenges for deployment in resource-constrained
environments. Knowledge distillation, a widely-used technique for transferring
knowledge from a large teacher model to a small student model, presents a
promising approach for model compression. A significant remaining issue lies in
the major differences between teacher and student models, namely the
substantial capacity gap, mode averaging, and mode collapse, which pose
barriers during distillation. To address these issues, we introduce
Temporally Adaptive Interpolated Distillation (TAID), a novel
knowledge distillation approach that dynamically interpolates student and
teacher distributions through an adaptive intermediate distribution, gradually
shifting from the student's initial distribution towards the teacher's
distribution. We provide a theoretical analysis demonstrating TAID's ability to
prevent mode collapse and empirically show its effectiveness in addressing the
capacity gap while balancing mode averaging and mode collapse. Our
comprehensive experiments demonstrate TAID's superior performance across
various model sizes and architectures in both instruction tuning and
pre-training scenarios. Furthermore, we showcase TAID's practical impact by
developing two state-of-the-art compact foundation models:
TAID-LLM-1.5B for language tasks and TAID-VLM-2B for
vision-language tasks. These results demonstrate TAID's effectiveness in
creating high-performing and efficient models, advancing the development of
more accessible AI technologies.
Histoires Morales: A French Dataset for Assessing Moral Alignment
Authors: name: Thibaud Leteno, name: Irina Proskurina, name: Antoine Gourru, name: Julien Velcin, name: Charlotte Laclau, name: Guillaume Metzler, name: Christophe Gravier
Published: 2025-01-28 18:07:30

Aligning language models with human values is crucial, especially as they
become more integrated into everyday life. While models are often adapted to
user preferences, it is equally important to ensure they align with moral norms
and behaviours in real-world social situations. Despite significant progress in
languages like English and Chinese, French has seen little attention in this
area, leaving a gap in understanding how LLMs handle moral reasoning in this
language. To address this gap, we introduce Histoires Morales, a French dataset
derived from Moral Stories, created through translation and subsequently
refined with the assistance of native speakers to guarantee grammatical
accuracy and adaptation to the French cultural context. We also rely on
annotations of the moral values within the dataset to ensure their alignment
with French norms. Histoires Morales covers a wide range of social situations,
including differences in tipping practices, expressions of honesty in
relationships, and responsibilities toward animals. To foster future research,
we also conduct preliminary experiments on the alignment of multilingual models
on French and English data and the robustness of the alignment. We find that
while LLMs are generally aligned with human moral norms by default, they can be
easily influenced with user-preference optimization for both moral and immoral
data.
DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian
  Splat Generation
Authors: name: Chenguo Lin, name: Panwang Pan, name: Bangbang Yang, name: Zeming Li, name: Yadong Mu
Published: 2025-01-28 07:38:59

Recent advancements in 3D content generation from text or a single image
struggle with limited high-quality 3D datasets and inconsistency from 2D
multi-view generation. We introduce DiffSplat, a novel 3D generative framework
that natively generates 3D Gaussian splats by taming large-scale text-to-image
diffusion models. It differs from previous 3D generative models by effectively
utilizing web-scale 2D priors while maintaining 3D consistency in a unified
model. To bootstrap the training, a lightweight reconstruction model is
proposed to instantly produce multi-view Gaussian splat grids for scalable
dataset curation. In conjunction with the regular diffusion loss on these
grids, a 3D rendering loss is introduced to facilitate 3D coherence across
arbitrary views. The compatibility with image diffusion models enables seamless
adaptions of numerous techniques for image generation to the 3D realm.
Extensive experiments reveal the superiority of DiffSplat in text- and
image-conditioned generation tasks and downstream applications. Thorough
ablation studies validate the efficacy of each critical design choice and
provide insights into the underlying mechanism.
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model
  Post-training
Authors: name: Tianzhe Chu, name: Yuexiang Zhai, name: Jihan Yang, name: Shengbang Tong, name: Saining Xie, name: Dale Schuurmans, name: Quoc V. Le, name: Sergey Levine, name: Yi Ma
Published: 2025-01-28 18:59:44

Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used
post-training techniques for foundation models. However, their roles in
enhancing model generalization capabilities remain unclear. This paper studies
the difference between SFT and RL on generalization and memorization, focusing
on text-based rule variants and visual variants. We introduce GeneralPoints, an
arithmetic reasoning card game, and adopt V-IRL, a real-world navigation
environment, to assess how models trained with SFT and RL generalize to unseen
variants in both textual and visual domains. We show that RL, especially when
trained with an outcome-based reward, generalizes across both rule-based
textual and visual variants. SFT, in contrast, tends to memorize training data
and struggles to generalize out-of-distribution scenarios. Further analysis
reveals that RL improves the model's underlying visual recognition
capabilities, contributing to its enhanced generalization in the visual domain.
Despite RL's superior generalization, we show that SFT remains essential for
effective RL training; SFT stabilizes the model's output format, enabling
subsequent RL to achieve its performance gains. These findings demonstrates the
capability of RL for acquiring generalizable knowledge in complex, multi-modal
tasks.
Optimizing Large Language Model Training Using FP4 Quantization
Authors: name: Ruizhe Wang, name: Yeyun Gong, name: Xiao Liu, name: Guoshuai Zhao, name: Ziyue Yang, name: Baining Guo, name: Zhengjun Zha, name: Peng Cheng
Published: 2025-01-28 18:04:50

The growing computational demands of training large language models (LLMs)
necessitate more efficient methods. Quantized training presents a promising
solution by enabling low-bit arithmetic operations to reduce these costs. While
FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge
due to significant quantization errors and limited representational capacity.
This work introduces the first FP4 training framework for LLMs, addressing
these challenges with two key innovations: a differentiable quantization
estimator for precise weight updates and an outlier clamping and compensation
strategy to prevent activation collapse. To ensure stability, the framework
integrates a mixed-precision training scheme and vector-wise quantization.
Experimental results demonstrate that our FP4 framework achieves accuracy
comparable to BF16 and FP8, with minimal degradation, scaling effectively to
13B-parameter LLMs trained on up to 100B tokens. With the emergence of
next-generation hardware supporting FP4, our framework sets a foundation for
efficient ultra-low precision training.
Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
Authors: name: Hongzhi Huang, name: Defa Zhu, name: Banggu Wu, name: Yutao Zeng, name: Ya Wang, name: Qiyang Min, name: Xun Zhou
Published: 2025-01-28 14:15:42

Tokenization is a fundamental component of large language models (LLMs), yet
its influence on model scaling and performance is not fully explored. In this
paper, we introduce Over-Tokenized Transformers, a novel framework that
decouples input and output vocabularies to improve language modeling
performance. Specifically, our approach scales up input vocabularies to
leverage multi-gram tokens. Through extensive experiments, we uncover a
log-linear relationship between input vocabulary size and training loss,
demonstrating that larger input vocabularies consistently enhance model
performance, regardless of model size. Using a large input vocabulary, we
achieve performance comparable to double-sized baselines with no additional
cost. Our findings highlight the importance of tokenization in scaling laws and
provide practical insight for tokenizer design, paving the way for more
efficient and powerful LLMs.