Orionnnn/hub-stats huggingface dataset:
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE
Authors: name: Haiduo Huang, name: Fuwei Yang, name: Zhenhua Liu, name: Yixing Xu, name: Jinze Li, name: Yang Liu, name: Xuanwu Yin, name: Dong Li, name: Pengju Ren, name: Emad Barsoum
Published: 2025-02-10 09:24:06

Speculative decoding (SD) accelerates large language model inference by using
a smaller draft model to predict multiple tokens, which are then verified in
parallel by the larger target model. However, the limited capacity of the draft
model often necessitates tree-based sampling to improve prediction accuracy,
where multiple candidates are generated at each step. We identify a key
limitation in this approach: the candidates at the same step are derived from
the same representation, limiting diversity and reducing overall effectiveness.
To address this, we propose Jakiro, leveraging Mixture of Experts (MoE), where
independent experts generate diverse predictions, effectively decoupling
correlations among candidates. Furthermore, we introduce a hybrid inference
strategy, combining autoregressive decoding for initial tokens with parallel
decoding for subsequent stages, and enhance the latter with contrastive
mechanism in features to improve accuracy. Our method significantly boosts
prediction accuracy and achieves higher inference speedups. Extensive
experiments across diverse models validate the effectiveness and robustness of
our approach, establishing a new SOTA in speculative decoding. Our codes are
available at https://github.com/haiduo/Jakiro.
InSTA: Towards Internet-Scale Training For Agents
Authors: name: Brandon Trabucco, name: Gunnar Sigurdsson, name: Robinson Piramuthu, name: Ruslan Salakhutdinov
Published: 2025-02-10 18:54:05

The predominant approach for training web navigation agents is to gather
human demonstrations for a set of popular websites and hand-written tasks, but
it is becoming clear that human data is an inefficient resource. We develop a
pipeline to facilitate internet-scale training for agents without laborious
human annotations. In the first stage, an LLM annotates 150k sites with agentic
tasks. In the next stage, LLM agents complete tasks and produce trajectories.
In the final stage, an LLM filters trajectories by judging their success.
Language models are powerful data curation tools, identifying harmful content
with an accuracy of 97%, judging successful trajectories with an accuracy of
82.6%, and producing effective data. We train agents based on Qwen 3 1.7B that
are competitive with frontier LLMs as web agents, while being smaller and
faster. Our top agent reaches a success rate of 56.9%, outperforming the data
collection policy Qwen 3 235B, a 235 times larger Llama 4 Maverick, and
reaching 94.7% of the performance of Gemini 2.5 Flash. We are releasing code,
models and data at: https://data-for-agents.github.io.
Embodied Red Teaming for Auditing Robotic Foundation Models
Authors: name: Sathwik Karnik, name: Zhang-Wei Hong, name: Nishant Abhangi, name: Yen-Chen Lin, name: Tsun-Hsuan Wang, name: Christophe Dupuy, name: Rahul Gupta, name: Pulkit Agrawal
Published: 2024-11-27 18:57:26

Language-conditioned robot models have the potential to enable robots to
perform a wide range of tasks based on natural language instructions. However,
assessing their safety and effectiveness remains challenging because it is
difficult to test all the different ways a single task can be phrased. Current
benchmarks have two key limitations: they rely on a limited set of
human-generated instructions, missing many challenging cases, and focus only on
task performance without assessing safety, such as avoiding damage. To address
these gaps, we introduce Embodied Red Teaming (ERT), a new evaluation method
that generates diverse and challenging instructions to test these models. ERT
uses automated red teaming techniques with Vision Language Models (VLMs) to
create contextually grounded, difficult instructions. Experimental results show
that state-of-the-art language-conditioned robot models fail or behave unsafely
on ERT-generated instructions, underscoring the shortcomings of current
benchmarks in evaluating real-world performance and safety. Code and videos are
available at: https://s-karnik.github.io/embodied-red-team-project-page.
CODESIM: Multi-Agent Code Generation and Problem Solving through
  Simulation-Driven Planning and Debugging
Authors: name: Md. Ashraful Islam, name: Mohammed Eunus Ali, name: Md Rizwan Parvez
Published: 2025-02-08 18:43:59

Large Language Models (LLMs) have made significant strides in code generation
and problem solving. Current approaches employ external tool-based iterative
debuggers that use compiler or other tool-based runtime feedback to refine
coarse programs generated by various methods. However, the effectiveness of
these approaches heavily relies on the quality of the initial code generation,
which remains an open challenge. In this paper, we introduce CodeSim, a novel
multi-agent code generation framework that comprehensively addresses the stages
of program synthesis-planning, coding, and debugging-through a human-like
perception approach. As human verifies their understanding of any algorithms
through visual simulation, CodeSim uniquely features a method of plan
verification and internal debugging through the step-by-step simulation of
input/output. Extensive experiments across seven challenging competitive
problem-solving and program synthesis benchmarks demonstrate CodeSim's
remarkable code generation capabilities. Our framework achieves new
state-of-the-art (pass@1) results-(HumanEval 95.1%, MBPP 90.7%, APPS 22%, and
CodeContests 29.1%). Furthermore, our method shows potential for even greater
enhancement when cascaded with external debuggers. To facilitate further
research and development in this area, we have open-sourced our framework in
this link (https://kagnlp.github.io/codesim.github.io/).
The Curse of Depth in Large Language Models
Authors: name: Wenfang Sun, name: Xinyuan Song, name: Pengxiang Li, name: Lu Yin, name: Yefeng Zheng, name: Shiwei Liu
Published: 2025-02-09 07:03:36

In this paper, we introduce the Curse of Depth, a concept that highlights,
explains, and addresses the recent observation in modern Large Language
Models(LLMs) where nearly half of the layers are less effective than expected.
We first confirm the wide existence of this phenomenon across the most popular
families of LLMs such as Llama, Mistral, DeepSeek, and Qwen. Our analysis,
theoretically and empirically, identifies that the underlying reason for the
ineffectiveness of deep layers in LLMs is the widespread usage of Pre-Layer
Normalization (Pre-LN). While Pre-LN stabilizes the training of Transformer
LLMs, its output variance exponentially grows with the model depth, which
undesirably causes the derivative of the deep Transformer blocks to be an
identity matrix, and therefore barely contributes to the training. To resolve
this training pitfall, we propose LayerNorm Scaling, which scales the variance
of output of the layer normalization inversely by the square root of its depth.
This simple modification mitigates the output variance explosion of deeper
Transformer layers, improving their contribution. Our experimental results,
spanning model sizes from 130M to 1B, demonstrate that LayerNorm Scaling
significantly enhances LLM pre-training performance compared to Pre-LN.
Moreover, this improvement seamlessly carries over to supervised fine-tuning.
All these gains can be attributed to the fact that LayerNorm Scaling enables
deeper layers to contribute more effectively during training.
Training Language Models for Social Deduction with Multi-Agent
  Reinforcement Learning
Authors: name: Bidipta Sarkar, name: Warren Xia, name: C. Karen Liu, name: Dorsa Sadigh
Published: 2025-02-09 22:44:45

Communicating in natural language is a powerful tool in multi-agent settings,
as it enables independent agents to share information in partially observable
settings and allows zero-shot coordination with humans. However, most prior
works are limited as they either rely on training with large amounts of human
demonstrations or lack the ability to generate natural and useful communication
strategies. In this work, we train language models to have productive
discussions about their environment in natural language without any human
demonstrations. We decompose the communication problem into listening and
speaking. Our key idea is to leverage the agent's goal to predict useful
information about the world as a dense reward signal that guides communication.
Specifically, we improve a model's listening skills by training them to predict
information about the environment based on discussions, and we simultaneously
improve a model's speaking skills with multi-agent reinforcement learning by
rewarding messages based on their influence on other agents. To investigate the
role and necessity of communication in complex social settings, we study an
embodied social deduction game based on Among Us, where the key question to
answer is the identity of an adversarial imposter. We analyze emergent
behaviors due to our technique, such as accusing suspects and providing
evidence, and find that it enables strong discussions, doubling the win rates
compared to standard RL. We release our code and models at
https://socialdeductionllm.github.io/
SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
  Annotators
Authors: name: Daniil Moskovskiy, name: Nikita Sushko, name: Sergey Pletenev, name: Elena Tutubalina, name: Alexander Panchenko
Published: 2025-02-10 12:30:25

Existing approaches to multilingual text detoxification are hampered by the
scarcity of parallel multilingual datasets. In this work, we introduce a
pipeline for the generation of multilingual parallel detoxification data. We
also introduce SynthDetoxM, a manually collected and synthetically generated
multilingual parallel text detoxification dataset comprising 16,000
high-quality detoxification sentence pairs across German, French, Spanish and
Russian. The data was sourced from different toxicity evaluation datasets and
then rewritten with nine modern open-source LLMs in few-shot setting. Our
experiments demonstrate that models trained on the produced synthetic datasets
have superior performance to those trained on the human-annotated
MultiParaDetox dataset even in data limited setting. Models trained on
SynthDetoxM outperform all evaluated LLMs in few-shot setting. We release our
dataset and code to help further research in multilingual text detoxification.
DreamDPO: Aligning Text-to-3D Generation with Human Preferences via
  Direct Preference Optimization
Authors: name: Zhenglin Zhou, name: Xiaobo Xia, name: Fan Ma, name: Hehe Fan, name: Yi Yang, name: Tat-Seng Chua
Published: 2025-02-05 11:03:08

Text-to-3D generation automates 3D content creation from textual
descriptions, which offers transformative potential across various fields.
However, existing methods often struggle to align generated content with human
preferences, limiting their applicability and flexibility. To address these
limitations, in this paper, we propose DreamDPO, an optimization-based
framework that integrates human preferences into the 3D generation process,
through direct preference optimization. Practically, DreamDPO first constructs
pairwise examples, then compare their alignment with human preferences using
reward or large multimodal models, and lastly optimizes the 3D representation
with a preference-driven loss function. By leveraging pairwise comparison to
reflect preferences, DreamDPO reduces reliance on precise pointwise quality
evaluations while enabling fine-grained controllability through
preference-guided optimization. Experiments demonstrate that DreamDPO achieves
competitive results, and provides higher-quality and more controllable 3D
content compared to existing methods. The code and models will be open-sourced.
Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
  Generation
Authors: name: Chenkai Xu, name: Xu Wang, name: Zhenyi Liao, name: Yishun Li, name: Tianqi Hou, name: Zhijie Deng
Published: 2025-02-08 02:52:25

There has been increasing research interest in building unified multimodal
understanding and generation models, among which Show-o stands as a notable
representative, demonstrating great promise for both text-to-image and
image-to-text generation. The inference of Show-o involves progressively
denoising image tokens and autoregressively decoding text tokens, and hence,
unfortunately, suffers from inefficiency issues from both sides. This paper
introduces Show-o Turbo to bridge the gap. We first identify a unified
denoising perspective for the generation of images and text in Show-o based on
the parallel decoding of text tokens. We then propose to extend consistency
distillation (CD), a qualified approach for shortening the denoising process of
diffusion models, to the multimodal denoising trajectories of Show-o. We
introduce a trajectory segmentation strategy and a curriculum learning
procedure to improve the training convergence. Empirically, in text-to-image
generation, Show-o Turbo displays a GenEval score of 0.625 at 4 sampling steps
without using classifier-free guidance (CFG), outperforming that of the
original Show-o with 8 steps and CFG; in image-to-text generation, Show-o Turbo
exhibits a 1.5x speedup without significantly sacrificing performance. The code
is available at https://github.com/zhijie-group/Show-o-Turbo.
MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents
Authors: name: Jiabin Tang, name: Tianyu Fan, name: Chao Huang
Published: 2025-02-09 16:53:56

Large Language Model (LLM) Agents have demonstrated remarkable capabilities
in task automation and intelligent decision-making, driving the widespread
adoption of agent development frameworks such as LangChain and AutoGen.
However, these frameworks predominantly serve developers with extensive
technical expertise - a significant limitation considering that only 0.03 % of
the global population possesses the necessary programming skills. This stark
accessibility gap raises a fundamental question: Can we enable everyone,
regardless of technical background, to build their own LLM agents using natural
language alone? To address this challenge, we introduce MetaChain-a
Fully-Automated and highly Self-Developing framework that enables users to
create and deploy LLM agents through Natural Language Alone. Operating as an
autonomous Agent Operating System, MetaChain comprises four key components: i)
Agentic System Utilities, ii) LLM-powered Actionable Engine, iii) Self-Managing
File System, and iv) Self-Play Agent Customization module. This lightweight yet
powerful system enables efficient and dynamic creation and modification of
tools, agents, and workflows without coding requirements or manual
intervention. Beyond its code-free agent development capabilities, MetaChain
also serves as a versatile multi-agent system for General AI Assistants.
Comprehensive evaluations on the GAIA benchmark demonstrate MetaChain's
effectiveness in generalist multi-agent tasks, surpassing existing
state-of-the-art methods. Furthermore, MetaChain's Retrieval-Augmented
Generation (RAG)-related capabilities have shown consistently superior
performance compared to many alternative LLM-based solutions.
Matryoshka Quantization
Authors: name: Pranav Nair, name: Puranjay Datta, name: Jeff Dean, name: Prateek Jain, name: Aditya Kusupati
Published: 2025-02-10 18:59:10

Quantizing model weights is critical for reducing the communication and
inference costs of large models. However, quantizing models -- especially to
low precisions like int4 or int2 -- requires a trade-off in model quality;
int2, in particular, is known to severely degrade model quality. Consequently,
practitioners are often forced to maintain multiple models with different
quantization levels or serve a single model that best satisfies the
quality-latency trade-off. On the other hand, integer data types, such as int8,
inherently possess a nested (Matryoshka) structure where smaller bit-width
integers, like int4 or int2, are nested within the most significant bits. This
paper proposes Matryoshka Quantization (MatQuant), a novel multi-scale
quantization technique that addresses the challenge of needing multiple
quantized models. It allows training and maintaining just one model, which can
then be served at different precision levels. Furthermore, due to the
co-training and co-distillation regularization provided by MatQuant, the int2
precision models extracted by MatQuant can be up to 10% more accurate than
standard int2 quantization (using techniques like QAT or OmniQuant). This
represents significant progress in model quantization, demonstrated by the fact
that, with the same recipe, an int2 FFN-quantized Gemma-2 9B model is more
accurate than an int8 FFN-quantized Gemma-2 2B model.
Lumina-Video: Efficient and Flexible Video Generation with Multi-scale
  Next-DiT
Authors: name: Dongyang Liu, name: Shicheng Li, name: Yutong Liu, name: Zhen Li, name: Kai Wang, name: Xinyue Li, name: Qi Qin, name: Yufei Liu, name: Yi Xin, name: Zhongyu Li, name: Bin Fu, name: Chenyang Si, name: Yuewen Cao, name: Conghui He, name: Ziwei Liu, name: Yu Qiao, name: Qibin Hou, name: Hongsheng Li, name: Peng Gao
Published: 2025-02-10 18:58:11

Recent advancements have established Diffusion Transformers (DiTs) as a
dominant framework in generative modeling. Building on this success,
Lumina-Next achieves exceptional performance in the generation of
photorealistic images with Next-DiT. However, its potential for video
generation remains largely untapped, with significant challenges in modeling
the spatiotemporal complexity inherent to video data. To address this, we
introduce Lumina-Video, a framework that leverages the strengths of Next-DiT
while introducing tailored solutions for video synthesis. Lumina-Video
incorporates a Multi-scale Next-DiT architecture, which jointly learns multiple
patchifications to enhance both efficiency and flexibility. By incorporating
the motion score as an explicit condition, Lumina-Video also enables direct
control of generated videos' dynamic degree. Combined with a progressive
training scheme with increasingly higher resolution and FPS, and a multi-source
training scheme with mixed natural and synthetic data, Lumina-Video achieves
remarkable aesthetic quality and motion smoothness at high training and
inference efficiency. We additionally propose Lumina-V2A, a video-to-audio
model based on Next-DiT, to create synchronized sounds for generated videos.
Codes are released at https://www.github.com/Alpha-VLLM/Lumina-Video.
History-Guided Video Diffusion
Authors: name: Kiwhan Song, name: Boyuan Chen, name: Max Simchowitz, name: Yilun Du, name: Russ Tedrake, name: Vincent Sitzmann
Published: 2025-02-10 18:44:25

Classifier-free guidance (CFG) is a key technique for improving conditional
generation in diffusion models, enabling more accurate control while enhancing
sample quality. It is natural to extend this technique to video diffusion,
which generates video conditioned on a variable number of context frames,
collectively referred to as history. However, we find two key challenges to
guiding with variable-length history: architectures that only support
fixed-size conditioning, and the empirical observation that CFG-style history
dropout performs poorly. To address this, we propose the Diffusion Forcing
Transformer (DFoT), a video diffusion architecture and theoretically grounded
training objective that jointly enable conditioning on a flexible number of
history frames. We then introduce History Guidance, a family of guidance
methods uniquely enabled by DFoT. We show that its simplest form, vanilla
history guidance, already significantly improves video generation quality and
temporal consistency. A more advanced method, history guidance across time and
frequency further enhances motion dynamics, enables compositional
generalization to out-of-distribution history, and can stably roll out
extremely long videos. Website: https://boyuan.space/history-guidance
CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for
  Zero-Shot Customized Video Diffusion Transformers
Authors: name: D. She, name: Mushui Liu, name: Jingxuan Pang, name: Jin Wang, name: Zhen Yang, name: Wanggui He, name: Guanghao Zhang, name: Yi Wang, name: Qihan Huang, name: Haobin Tang, name: Yunlong Yu, name: Siming Fu
Published: 2025-02-10 14:50:32

Customized generation has achieved significant progress in image synthesis,
yet personalized video generation remains challenging due to temporal
inconsistencies and quality degradation. In this paper, we introduce
CustomVideoX, an innovative framework leveraging the video diffusion
transformer for personalized video generation from a reference image.
CustomVideoX capitalizes on pre-trained video networks by exclusively training
the LoRA parameters to extract reference features, ensuring both efficiency and
adaptability. To facilitate seamless interaction between the reference image
and video content, we propose 3D Reference Attention, which enables direct and
simultaneous engagement of reference image features with all video frames
across spatial and temporal dimensions. To mitigate the excessive influence of
reference image features and textual guidance on generated video content during
inference, we implement the Time-Aware Reference Attention Bias (TAB) strategy,
dynamically modulating reference bias over different time steps. Additionally,
we introduce the Entity Region-Aware Enhancement (ERAE) module, aligning highly
activated regions of key entity tokens with reference feature injection by
adjusting attention bias. To thoroughly evaluate personalized video generation,
we establish a new benchmark, VideoBench, comprising over 50 objects and 100
prompts for extensive assessment. Experimental results show that CustomVideoX
significantly outperforms existing methods in terms of video consistency and
quality.
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time
  Scaling
Authors: name: Runze Liu, name: Junqi Gao, name: Jian Zhao, name: Kaiyan Zhang, name: Xiu Li, name: Biqing Qi, name: Wanli Ouyang, name: Bowen Zhou
Published: 2025-02-10 17:30:23

Test-Time Scaling (TTS) is an important method for improving the performance
of Large Language Models (LLMs) by using additional computation during the
inference phase. However, current studies do not systematically analyze how
policy models, Process Reward Models (PRMs), and problem difficulty influence
TTS. This lack of analysis limits the understanding and practical use of TTS
methods. In this paper, we focus on two core questions: (1) What is the optimal
approach to scale test-time computation across different policy models, PRMs,
and problem difficulty levels? (2) To what extent can extended computation
improve the performance of LLMs on complex tasks, and can smaller language
models outperform larger ones through this approach? Through comprehensive
experiments on MATH-500 and challenging AIME24 tasks, we have the following
observations: (1) The compute-optimal TTS strategy is highly dependent on the
choice of policy model, PRM, and problem difficulty. (2) With our
compute-optimal TTS strategy, extremely small policy models can outperform
larger models. For example, a 1B LLM can exceed a 405B LLM on MATH-500.
Moreover, on both MATH-500 and AIME24, a 0.5B LLM outperforms GPT-4o, a 3B LLM
surpasses a 405B LLM, and a 7B LLM beats o1 and DeepSeek-R1, while with higher
inference efficiency. These findings show the significance of adapting TTS
strategies to the specific characteristics of each task and model and indicate
that TTS is a promising approach for enhancing the reasoning abilities of LLMs.