Orionnnn/hub-stats huggingface dataset:
CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic
  Environments
Authors: name: Mingcong Lei, name: Ge Wang, name: Yiming Zhao, name: Zhixin Mai, name: Qing Zhao, name: Yao Guo, name: Zhen Li, name: Shuguang Cui, name: Yatong Han, name: Jinke Ren
Published: 2025-03-02 04:50:59

Large Language Models (LLMs) exhibit remarkable capabilities in the
hierarchical decomposition of complex tasks through semantic reasoning.
However, their application in embodied systems faces challenges in ensuring
reliable execution of subtask sequences and achieving one-shot success in
long-term task completion. To address these limitations in dynamic
environments, we propose Closed-Loop Embodied Agent (CLEA) -- a novel
architecture incorporating four specialized open-source LLMs with functional
decoupling for closed-loop task management. The framework features two core
innovations: (1) Interactive task planner that dynamically generates executable
subtasks based on the environmental memory, and (2) Multimodal execution critic
employing an evaluation framework to conduct a probabilistic assessment of
action feasibility, triggering hierarchical re-planning mechanisms when
environmental perturbations exceed preset thresholds. To validate CLEA's
effectiveness, we conduct experiments in a real environment with manipulable
objects, using two heterogeneous robots for object search, manipulation, and
search-manipulation integration tasks. Across 12 task trials, CLEA outperforms
the baseline model, achieving a 67.3% improvement in success rate and a 52.8%
increase in task completion rate. These results demonstrate that CLEA
significantly enhances the robustness of task planning and execution in dynamic
environments.
Speculative Ad-hoc Querying
Authors: name: Haoyu Li, name: Srikanth Kandula, name: Maria Angels de Luis Balaguer, name: Aditya Akella, name: Venkat Arun
Published: 2025-03-02 03:44:31

Analyzing large datasets requires responsive query execution, but executing
SQL queries on massive datasets can be slow. This paper explores whether query
execution can begin even before the user has finished typing, allowing results
to appear almost instantly. We propose SpeQL, a system that leverages Large
Language Models (LLMs) to predict likely queries based on the database schema,
the user's past queries, and their incomplete query. Since exact query
prediction is infeasible, SpeQL speculates on partial queries in two ways: 1)
it predicts the query structure to compile and plan queries in advance, and 2)
it precomputes smaller temporary tables that are much smaller than the original
database, but are still predicted to contain all information necessary to
answer the user's final query. Additionally, SpeQL continuously displays
results for speculated queries and subqueries in real time, aiding exploratory
analysis. A utility/user study showed that SpeQL improved task completion time,
and participants reported that its speculative display of results helped them
discover patterns in the data more quickly. In the study, SpeQL improves user's
query latency by up to 289times and kept the overhead reasonable, at 4$
per hour.
CodeArena: A Collective Evaluation Platform for LLM Code Generation
Authors: name: Mingzhe Du, name: Anh Tuan Luu, name: Bin Ji, name: Xiaobao Wu, name: Dong Huang, name: Terry Yue Zhuo, name: Qian Liu, name: See-Kiong Ng
Published: 2025-03-03 08:31:16

Large Language Models (LLMs) have reshaped code generation by synergizing
their exceptional comprehension of natural language and programming syntax,
thereby substantially boosting developer productivity. These advancements have
prompted numerous efforts to quantitatively evaluate their coding capabilities.
However, persistent challenges, such as benchmark leakage, data dissipation,
and limited system accessibility, continue to impede a timely and accurate
assessment. To address these limitations, we introduce CodeArena, an online
evaluation framework tailored for LLM code generation. The key innovation is a
collective evaluation mechanism, which dynamically recalibrates individual
model scores based on the holistic performance of all participating models,
mitigating score biases caused by widespread benchmark leakage. In addition,
CodeArena ensures open access to all submitted solutions and test cases and
provides automation-friendly APIs to streamline the code evaluation workflow.
Our main contributions are: (1) a collective evaluation system for unbiased
assessment, (2) a public repository of solutions and test cases, and (3)
automation-ready APIs for seamless integration.
Qilin: A Multimodal Information Retrieval Dataset with APP-level User
  Sessions
Authors: name: Jia Chen, name: Qian Dong, name: Haitao Li, name: Xiaohui He, name: Yan Gao, name: Shaosheng Cao, name: Yi Wu, name: Ping Yang, name: Chen Xu, name: Yao Hu, name: Qingyao Ai, name: Yiqun Liu
Published: 2025-03-01 14:15:00

User-generated content (UGC) communities, especially those featuring
multimodal content, improve user experiences by integrating visual and textual
information into results (or items). The challenge of improving user
experiences in complex systems with search and recommendation (S\&R) services
has drawn significant attention from both academia and industry these years.
However, the lack of high-quality datasets has limited the research progress on
multimodal S\&R. To address the growing need for developing better S\&R
services, we present a novel multimodal information retrieval dataset in this
paper, namely Qilin. The dataset is collected from Xiaohongshu, a popular
social platform with over 300 million monthly active users and an average
search penetration rate of over 70\%. In contrast to existing datasets,
Qilin offers a comprehensive collection of user sessions with
heterogeneous results like image-text notes, video notes, commercial notes, and
direct answers, facilitating the development of advanced multimodal neural
retrieval models across diverse task settings. To better model user
satisfaction and support the analysis of heterogeneous user behaviors, we also
collect extensive APP-level contextual signals and genuine user feedback.
Notably, Qilin contains user-favored answers and their referred results for
search requests triggering the Deep Query Answering (DQA) module. This allows
not only the training \& evaluation of a Retrieval-augmented Generation (RAG)
pipeline, but also the exploration of how such a module would affect users'
search behavior. Through comprehensive analysis and experiments, we provide
interesting findings and insights for further improving S\&R systems. We hope
that Qilin will significantly contribute to the advancement of
multimodal content platforms with S\&R services in the future.
Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
Authors: name: Jiantao Lin, name: Xin Yang, name: Meixi Chen, name: Yingjie Xu, name: Dongyu Yan, name: Leyi Wu, name: Xinli Xu, name: Lie XU, name: Shunsi Zhang, name: Ying-Cong Chen
Published: 2025-03-03 10:07:19

Diffusion models have achieved great success in generating 2D images.
However, the quality and generalizability of 3D content generation remain
limited. State-of-the-art methods often require large-scale 3D assets for
training, which are challenging to collect. In this work, we introduce
Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient
framework for generating, editing, and enhancing 3D objects by repurposing a
well-trained 2D image diffusion model for 3D generation. Specifically, we
fine-tune a diffusion model to generate ''3D Bundle Image'', a tiled
representation composed of multi-view images and their corresponding normal
maps. The normal maps are then used to reconstruct a 3D mesh, and the
multi-view images provide texture mapping, resulting in a complete 3D model.
This simple method effectively transforms the 3D generation problem into a 2D
image generation task, maximizing the utilization of knowledge in pretrained
diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is
compatible with various diffusion model techniques, enabling advanced features
such as 3D editing, mesh and texture enhancement, etc. Through extensive
experiments, we demonstrate the effectiveness of our approach, showcasing its
ability to produce high-quality 3D models efficiently.
Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models
Authors: name: Jay Zhangjie Wu, name: Yuxuan Zhang, name: Haithem Turki, name: Xuanchi Ren, name: Jun Gao, name: Mike Zheng Shou, name: Sanja Fidler, name: Zan Gojcic, name: Huan Ling
Published: 2025-03-03 17:58:33

Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D
reconstruction and novel-view synthesis task. However, achieving photorealistic
rendering from extreme novel viewpoints remains challenging, as artifacts
persist across representations. In this work, we introduce Difix3D+, a novel
pipeline designed to enhance 3D reconstruction and novel-view synthesis through
single-step diffusion models. At the core of our approach is Difix, a
single-step image diffusion model trained to enhance and remove artifacts in
rendered novel views caused by underconstrained regions of the 3D
representation. Difix serves two critical roles in our pipeline. First, it is
used during the reconstruction phase to clean up pseudo-training views that are
rendered from the reconstruction and then distilled back into 3D. This greatly
enhances underconstrained regions and improves the overall 3D representation
quality. More importantly, Difix also acts as a neural enhancer during
inference, effectively removing residual artifacts arising from imperfect 3D
supervision and the limited capacity of current reconstruction models. Difix3D+
is a general solution, a single model compatible with both NeRF and 3DGS
representations, and it achieves an average 2times improvement in FID score
over baselines while maintaining 3D consistency.
VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video
  Generation
Authors: name: Wenhao Wang, name: Yi Yang
Published: 2025-03-03 17:00:36

Text-to-video generative models convert textual prompts into dynamic visual
content, offering wide-ranging applications in film production, gaming, and
education. However, their real-world performance often falls short of user
expectations. One key reason is that these models have not been trained on
videos related to some topics users want to create. In this paper, we propose
VideoUFO, the first Video dataset specifically curated to align with Users'
FOcus in real-world scenarios. Beyond this, our VideoUFO also features: (1)
minimal (0.29%) overlap with existing video datasets, and (2) videos
searched exclusively via YouTube's official API under the Creative Commons
license. These two attributes provide future researchers with greater freedom
to broaden their training sources. The VideoUFO comprises over 1.09 million
video clips, each paired with both a brief and a detailed caption
(description). Specifically, through clustering, we first identify 1,291
user-focused topics from the million-scale real text-to-video prompt dataset,
VidProM. Then, we use these topics to retrieve videos from YouTube, split the
retrieved videos into clips, and generate both brief and detailed captions for
each clip. After verifying the clips with specified topics, we are left with
about 1.09 million video clips. Our experiments reveal that (1) current 16
text-to-video models do not achieve consistent performance across all
user-focused topics; and (2) a simple model trained on VideoUFO outperforms
others on worst-performing topics. The dataset is publicly available at
https://huggingface.co/datasets/WenhaoWang/VideoUFO under the CC BY 4.0
License.
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four
  Habits of Highly Effective STaRs
Authors: name: Kanishk Gandhi, name: Ayush Chakravarthy, name: Anikait Singh, name: Nathan Lile, name: Noah D. Goodman
Published: 2025-03-03 08:46:22

Test-time inference has emerged as a powerful paradigm for enabling language
models to ``think'' longer and more carefully about complex challenges, much
like skilled human experts. While reinforcement learning (RL) can drive
self-improvement in language models on verifiable tasks, some models exhibit
substantial gains while others quickly plateau. For instance, we find that
Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game
of Countdown. This discrepancy raises a critical question: what intrinsic
properties enable effective self-improvement? We introduce a framework to
investigate this question by analyzing four key cognitive behaviors --
verification, backtracking, subgoal setting, and backward chaining -- that both
expert human problem solvers and successful language models employ. Our study
reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama
initially lacks them. In systematic experimentation with controlled behavioral
datasets, we find that priming Llama with examples containing these reasoning
behaviors enables substantial improvements during RL, matching or exceeding
Qwen's performance. Importantly, the presence of reasoning behaviors, rather
than correctness of answers, proves to be the critical factor -- models primed
with incorrect solutions containing proper reasoning patterns achieve
comparable performance to those trained on correct solutions. Finally,
leveraging continued pretraining with OpenWebMath data, filtered to amplify
reasoning behaviors, enables the Llama model to match Qwen's self-improvement
trajectory. Our findings establish a fundamental relationship between initial
reasoning behaviors and the capacity for improvement, explaining why some
language models effectively utilize additional computation while others
plateau.
Large-Scale Data Selection for Instruction Tuning
Authors: name: Hamish Ivison, name: Muru Zhang, name: Faeze Brahman, name: Pang Wei Koh, name: Pradeep Dasigi
Published: 2025-03-03 18:37:26

Selecting high-quality training data from a larger pool is a crucial step
when instruction-tuning language models, as carefully curated datasets often
produce models that outperform those trained on much larger, noisier datasets.
Automated data selection approaches for instruction-tuning are typically tested
by selecting small datasets (roughly 10k samples) from small pools (100-200k
samples). However, popular deployed instruction-tuned models often train on
hundreds of thousands to millions of samples, subsampled from even larger data
pools. We present a systematic study of how well data selection methods scale
to these settings, selecting up to 2.5M samples from pools of up to 5.8M
samples and evaluating across 7 diverse tasks. We show that many recently
proposed methods fall short of random selection in this setting (while using
more compute), and even decline in performance when given access to larger
pools of data to select over. However, we find that a variant of
representation-based data selection (RDS+), which uses weighted mean pooling of
pretrained LM hidden states, consistently outperforms more complex methods
across all settings tested -- all whilst being more compute-efficient. Our
findings highlight that the scaling properties of proposed automated selection
methods should be more closely examined. We release our code, data, and models
at https://github.com/hamishivi/automated-instruction-selection.
Visual-RFT: Visual Reinforcement Fine-Tuning
Authors: name: Ziyu Liu, name: Zeyi Sun, name: Yuhang Zang, name: Xiaoyi Dong, name: Yuhang Cao, name: Haodong Duan, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-03-03 18:16:32

Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1
learns from feedback on its answers, which is especially useful in applications
when fine-tuning data is scarce. Recent open-source work like DeepSeek-R1
demonstrates that reinforcement learning with verifiable reward is one key
direction in reproducing o1. While the R1-style model has demonstrated success
in language models, its application in multi-modal domains remains
under-explored. This work introduces Visual Reinforcement Fine-Tuning
(Visual-RFT), which further extends the application areas of RFT on visual
tasks. Specifically, Visual-RFT first uses Large Vision-Language Models (LVLMs)
to generate multiple responses containing reasoning tokens and final answers
for each input, and then uses our proposed visual perception verifiable reward
functions to update the model via the policy optimization algorithm such as
Group Relative Policy Optimization (GRPO). We design different verifiable
reward functions for different perception tasks, such as the Intersection over
Union (IoU) reward for object detection. Experimental results on fine-grained
image classification, few-shot object detection, reasoning grounding, as well
as open-vocabulary object detection benchmarks show the competitive performance
and advanced generalization ability of Visual-RFT compared with Supervised
Fine-tuning (SFT). For example, Visual-RFT improves accuracy by 24.3% over
the baseline in one-shot fine-grained image classification with around 100
samples. In few-shot object detection, Visual-RFT also exceeds the baseline by
21.9 on COCO's two-shot setting and 15.4 on LVIS. Our Visual-RFT represents
a paradigm shift in fine-tuning LVLMs, offering a data-efficient, reward-driven
approach that enhances reasoning and adaptability for domain-specific tasks.
DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with
  Dynamic Multi-Sequence Drafting
Authors: name: Kai Lv, name: Honglin Guo, name: Qipeng Guo, name: Xipeng Qiu
Published: 2025-03-02 08:27:48

Large language models (LLMs) exhibit exceptional performance across a wide
range of tasks; however, their token-by-token autoregressive generation process
significantly hinders inference speed. Speculative decoding presents a
promising draft-then-verify framework that reduces generation latency while
maintaining output distribution fidelity. Nevertheless, the draft model
introduces additional computational overhead, becoming a performance bottleneck
and increasing the time to first token (TTFT). Previous approaches to mitigate
draft model overhead have primarily relied on heuristics and generally failed
to match the quality of the draft language models. To address these challenges,
we propose DuoDecoding, a novel approach that strategically deploys the draft
and target models on the CPU and GPU respectively, enabling parallel decoding
while preserving draft quality. Our method incorporates a hardware-aware
optimal draft budget to minimize idle times and employs dynamic multi-sequence
drafting to enhance draft quality. Extensive experiments across seven tasks
show that DuoDecoding achieves up to 2.61x speedup in generation latency, while
reducing TTFT to 83% of that in conventional speculative decoding. The Code is
available at https://github.com/KaiLv69/DuoDecoding.
Predictive Data Selection: The Data That Predicts Is the Data That
  Teaches
Authors: name: Kashun Shum, name: Yuzhen Huang, name: Hongjian Zou, name: Ding Qi, name: Yixuan Liao, name: Xiaoxin Chen, name: Qian Liu, name: Junxian He
Published: 2025-03-02 09:21:28

Language model pretraining involves training on extensive corpora, where data
quality plays a pivotal role. In this work, we aim to directly estimate the
contribution of data during pretraining and select pretraining data in an
efficient manner. Specifically, we draw inspiration from recent findings
showing that compression efficiency (i.e., the normalized loss) of diverse
models on certain text correlates strongly with their downstream performance,
when the text domain aligns with the downstream benchmark (Huang et al., 2024).
Building on this observation, we hypothesize that data on which model losses
are predictive of downstream abilities also contribute effectively to learning.
To leverage this insight, we introduce data selection based on data's
Predictive strength (Preselect), a lightweight and efficient data selection
method that requires training and deploying only a fastText-based scorer.
Through comprehensive experiments with 1B and 3B parameter models, we
demonstrate that models trained on 30B tokens selected with PreSelect surpasses
the performance of a vanilla baseline trained on 300B tokens, achieving a 10x
reduction in compute requirements. Furthermore, PreSelect significantly
outperforms other competitive data selection baselines, such as DCLM and
FineWeb-Edu on a scale of 3B models trained on 100B tokens. We open-source our
trained data selection scorer along with the curated datasets at
https://github.com/hkust-nlp/PreSelect.
Multi-Turn Code Generation Through Single-Step Rewards
Authors: name: Arnav Kumar Jain, name: Gonzalo Gonzalez-Pumariega, name: Wayne Chen, name: Alexander M Rush, name: Wenting Zhao, name: Sanjiban Choudhury
Published: 2025-02-27 18:55:05

We address the problem of code generation from multi-turn execution feedback.
Existing methods either generate code without feedback or use complex,
hierarchical reinforcement learning to optimize multi-turn rewards. We propose
a simple yet scalable approach, muCode, that solves multi-turn code
generation using only single-step rewards. Our key insight is that code
generation is a one-step recoverable MDP, where the correct code can be
recovered from any intermediate code state in a single turn. muCode
iteratively trains both a generator to provide code solutions conditioned on
multi-turn execution feedback and a verifier to score the newly generated code.
Experimental evaluations show that our approach achieves significant
improvements over the state-of-the-art baselines. We provide analysis of the
design choices of the reward models and policy, and show the efficacy of
muCode at utilizing the execution feedback. Our code is available at
https://github.com/portal-cornell/muCode.
Preference Learning Unlocks LLMs' Psycho-Counseling Skills
Authors: name: Mian Zhang, name: Shaun M. Eack, name: Zhiyu Zoey Chen
Published: 2025-02-27 03:50:25

Applying large language models (LLMs) to assist in psycho-counseling is an
emerging and meaningful approach, driven by the significant gap between patient
needs and the availability of mental health support. However, current LLMs
struggle to consistently provide effective responses to client speeches,
largely due to the lack of supervision from high-quality real psycho-counseling
data, whose content is typically inaccessible due to client privacy concerns.
Furthermore, the quality of therapists' responses in available sessions can
vary significantly based on their professional training and experience.
Assessing the quality of therapists' responses remains an open challenge. In
this work, we address these challenges by first proposing a set of professional
and comprehensive principles to evaluate therapists' responses to client
speeches. Using these principles, we create a preference dataset,
PsychoCounsel-Preference, which contains 36k high-quality preference comparison
pairs. This dataset aligns with the preferences of professional
psychotherapists, providing a robust foundation for evaluating and improving
LLMs in psycho-counseling. Experiments on reward modeling and preference
learning demonstrate that PsychoCounsel-Preference is an excellent resource for
LLMs to acquire essential skills for responding to clients in a counseling
session. Our best-aligned model, PsychoCounsel-Llama3-8B, achieves an
impressive win rate of 87% against GPT-4o. We release PsychoCounsel-Preference,
PsychoCounsel-Llama3-8B and the reward model PsychoCounsel Llama3-8B-Reward to
facilitate the research of psycho-counseling with LLMs at:
https://hf.co/Psychotherapy-LLM.