Orionnnn/hub-stats huggingface dataset:
Taming the Titans: A Survey of Efficient LLM Inference Serving
Authors: name: Ranran Zhen, name: Juntao Li, name: Yixin Ji, name: Zhenlin Yang, name: Tong Liu, name: Qingrong Xia, name: Xinyu Duan, name: Zhefeng Wang, name: Baoxing Huai, name: Min Zhang
Published: 2025-04-28 12:14:02

Large Language Models (LLMs) for Generative AI have achieved remarkable
progress, evolving into sophisticated and versatile tools widely adopted across
various domains and applications. However, the substantial memory overhead
caused by their vast number of parameters, combined with the high computational
demands of the attention mechanism, poses significant challenges in achieving
low latency and high throughput for LLM inference services. Recent
advancements, driven by groundbreaking research, have significantly accelerated
progress in this field. This paper provides a comprehensive survey of these
methods, covering fundamental instance-level approaches, in-depth cluster-level
strategies, emerging scenario directions, and other miscellaneous but important
areas. At the instance level, we review model placement, request scheduling,
decoding length prediction, storage management, and the disaggregation
paradigm. At the cluster level, we explore GPU cluster deployment,
multi-instance load balancing, and cloud service solutions. For emerging
scenarios, we organize the discussion around specific tasks, modules, and
auxiliary methods. To ensure a holistic overview, we also highlight several
niche yet critical areas. Finally, we outline potential research directions to
further advance the field of LLM inference serving.
Softpick: No Attention Sink, No Massive Activations with Rectified
  Softmax
Authors: name: Zayd M. K. Zuhri, name: Erland Hilman Fuadi, name: Alham Fikri Aji
Published: 2025-04-29 17:36:18

We introduce softpick, a rectified, not sum-to-one, drop-in replacement for
softmax in transformer attention mechanisms that eliminates attention sink and
massive activations. Our experiments with 340M parameter models demonstrate
that softpick maintains performance parity with softmax on standard benchmarks
while achieving 0% sink rate. The softpick transformer produces hidden states
with significantly lower kurtosis (340 vs 33,510) and creates sparse attention
maps (46.97% sparsity). Models using softpick consistently outperform softmax
when quantized, with particularly pronounced advantages at lower bit
precisions. Our analysis and discussion shows how softpick has the potential to
open new possibilities for quantization, low-precision training, sparsity
optimization, pruning, and interpretability. Our code is available at
https://github.com/zaydzuhri/softpick-attention.
ReVision: High-Quality, Low-Cost Video Generation with Explicit 3D
  Physics Modeling for Complex Motion and Interaction
Authors: name: Qihao Liu, name: Ju He, name: Qihang Yu, name: Liang-Chieh Chen, name: Alan Yuille
Published: 2025-04-30 17:59:56

In recent years, video generation has seen significant advancements. However,
challenges still persist in generating complex motions and interactions. To
address these challenges, we introduce ReVision, a plug-and-play framework that
explicitly integrates parameterized 3D physical knowledge into a pretrained
conditional video generation model, significantly enhancing its ability to
generate high-quality videos with complex motion and interactions.
Specifically, ReVision consists of three stages. First, a video diffusion model
is used to generate a coarse video. Next, we extract a set of 2D and 3D
features from the coarse video to construct a 3D object-centric representation,
which is then refined by our proposed parameterized physical prior model to
produce an accurate 3D motion sequence. Finally, this refined motion sequence
is fed back into the same video diffusion model as additional conditioning,
enabling the generation of motion-consistent videos, even in scenarios
involving complex actions and interactions. We validate the effectiveness of
our approach on Stable Video Diffusion, where ReVision significantly improves
motion fidelity and coherence. Remarkably, with only 1.5B parameters, it even
outperforms a state-of-the-art video generation model with over 13B parameters
on complex video generation by a substantial margin. Our results suggest that,
by incorporating 3D physical knowledge, even a relatively small video diffusion
model can generate complex motions and interactions with greater realism and
controllability, offering a promising solution for physically plausible video
generation.
WebThinker: Empowering Large Reasoning Models with Deep Research
  Capability
Authors: name: Xiaoxi Li, name: Jiajie Jin, name: Guanting Dong, name: Hongjin Qian, name: Yutao Zhu, name: Yongkang Wu, name: Ji-Rong Wen, name: Zhicheng Dou
Published: 2025-04-30 16:25:25

Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate
impressive long-horizon reasoning capabilities. However, their reliance on
static internal knowledge limits their performance on complex,
knowledge-intensive tasks and hinders their ability to produce comprehensive
research reports requiring synthesis of diverse web information. To address
this, we propose WebThinker, a deep research agent that empowers LRMs
to autonomously search the web, navigate web pages, and draft research reports
during the reasoning process. WebThinker integrates a Deep Web
Explorer module, enabling LRMs to dynamically search, navigate, and extract
information from the web when encountering knowledge gaps. It also employs an
Autonomous Think-Search-and-Draft strategy, allowing the model to
seamlessly interleave reasoning, information gathering, and report writing in
real time. To further enhance research tool utilization, we introduce an
RL-based training strategy via iterative online Direct Preference
Optimization (DPO). Extensive experiments on complex reasoning benchmarks
(GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive)
demonstrate that WebThinker significantly outperforms existing methods and
strong proprietary systems. Our approach enhances LRM reliability and
applicability in complex scenarios, paving the way for more capable and
versatile deep research systems. The code is available at
https://github.com/RUC-NLPIR/WebThinker.
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language
  Models in Math
Authors: name: Haoran Xu, name: Baolin Peng, name: Hany Awadalla, name: Dongdong Chen, name: Yen-Chun Chen, name: Mei Gao, name: Young Jin Kim, name: Yunsheng Li, name: Liliang Ren, name: Yelong Shen, name: Shuohang Wang, name: Weijian Xu, name: Jianfeng Gao, name: Weizhu Chen
Published: 2025-04-30 00:04:35

Chain-of-Thought (CoT) significantly enhances formal reasoning capabilities
in Large Language Models (LLMs) by training them to explicitly generate
intermediate reasoning steps. While LLMs readily benefit from such techniques,
improving reasoning in Small Language Models (SLMs) remains challenging due to
their limited model capacity. Recent work by Deepseek-R1 demonstrates that
distillation from LLM-generated synthetic data can substantially improve the
reasoning ability of SLM. However, the detailed modeling recipe is not
disclosed. In this work, we present a systematic training recipe for SLMs that
consists of four steps: (1) large-scale mid-training on diverse distilled
long-CoT data, (2) supervised fine-tuning on high-quality long-CoT data, (3)
Rollout DPO leveraging a carefully curated preference dataset, and (4)
Reinforcement Learning (RL) with Verifiable Reward. We apply our method on
Phi-4-Mini, a compact 3.8B-parameter model. The resulting Phi-4-Mini-Reasoning
model exceeds, on math reasoning tasks, much larger reasoning models, e.g.,
outperforming DeepSeek-R1-Distill-Qwen-7B by 3.2 points and
DeepSeek-R1-Distill-Llama-8B by 7.7 points on Math-500. Our results validate
that a carefully designed training recipe, with large-scale high-quality CoT
data, is effective to unlock strong reasoning capabilities even in
resource-constrained small models.
COMPACT: COMPositional Atomic-to-Complex Visual Capability Tuning
Authors: name: Xindi Wu, name: Hee Seung Hwang, name: Polina Kirichenko, name: Olga Russakovsky
Published: 2025-04-30 17:57:22

Multimodal Large Language Models (MLLMs) excel at simple vision-language
tasks but struggle when faced with complex tasks that require multiple
capabilities, such as simultaneously recognizing objects, counting them, and
understanding their spatial relationships. This might be partially the result
of the fact that Visual Instruction Tuning (VIT), a critical training step for
MLLMs, has traditionally focused on scaling data volume, but not the
compositional complexity of training examples. We propose COMPACT
(COMPositional Atomic-to-complex visual Capability Tuning), which generates a
training dataset explicitly controlling for the compositional complexity of the
training examples. The data from COMPACT allows MLLMs to train on combinations
of atomic capabilities to learn complex capabilities more efficiently. Across
all benchmarks, COMPACT achieves comparable performance to the LLaVA-665k VIT
while using less than 10% of its data budget, and even outperforms it on
several, especially those involving complex multi-capability tasks. For
example, COMPACT achieves substantial 83.3% improvement on MMStar and 94.0%
improvement on MM-Vet compared to the full-scale VIT on particularly complex
questions that require four or more atomic capabilities. COMPACT offers a
scalable, data-efficient, visual compositional tuning recipe to improve on
complex visual-language tasks.
Phi-4-reasoning Technical Report
Authors: name: Marah Abdin, name: Sahaj Agarwal, name: Ahmed Awadallah, name: Vidhisha Balachandran, name: Harkirat Behl, name: Lingjiao Chen, name: Gustavo de Rosa, name: Suriya Gunasekar, name: Mojan Javaheripi, name: Neel Joshi, name: Piero Kauffmann, name: Yash Lara, name: Caio César Teodoro Mendes, name: Arindam Mitra, name: Besmira Nushi, name: Dimitris Papailiopoulos, name: Olli Saarikivi, name: Shital Shah, name: Vaishnavi Shrivastava, name: Vibhav Vineet, name: Yue Wu, name: Safoora Yousefi, name: Guoqing Zheng
Published: 2025-04-30 05:05:09

We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that
achieves strong performance on complex reasoning tasks. Trained via supervised
fine-tuning of Phi-4 on carefully curated set of "teachable" prompts-selected
for the right level of complexity and diversity-and reasoning demonstrations
generated using o3-mini, Phi-4-reasoning generates detailed reasoning chains
that effectively leverage inference-time compute. We further develop
Phi-4-reasoning-plus, a variant enhanced through a short phase of outcome-based
reinforcement learning that offers higher performance by generating longer
reasoning traces. Across a wide range of reasoning tasks, both models
outperform significantly larger open-weight models such as
DeepSeek-R1-Distill-Llama-70B model and approach the performance levels of full
DeepSeek-R1 model. Our comprehensive evaluations span benchmarks in math and
scientific reasoning, coding, algorithmic problem solving, planning, and
spatial understanding. Interestingly, we observe a non-trivial transfer of
improvements to general-purpose benchmarks as well. In this report, we provide
insights into our training data, our training methodologies, and our
evaluations. We show that the benefit of careful data curation for supervised
fine-tuning (SFT) extends to reasoning language models, and can be further
amplified by reinforcement learning (RL). Finally, our evaluation points to
opportunities for improving how we assess the performance and robustness of
reasoning models.
RoboVerse: Towards a Unified Platform, Dataset and Benchmark for
  Scalable and Generalizable Robot Learning
Authors: name: Haoran Geng, name: Feishi Wang, name: Songlin Wei, name: Yuyang Li, name: Bangjun Wang, name: Boshi An, name: Charlie Tianyue Cheng, name: Haozhe Lou, name: Peihao Li, name: Yen-Jen Wang, name: Yutong Liang, name: Dylan Goetting, name: Chaoyi Xu, name: Haozhe Chen, name: Yuxi Qian, name: Yiran Geng, name: Jiageng Mao, name: Weikang Wan, name: Mingtong Zhang, name: Jiangran Lyu, name: Siheng Zhao, name: Jiazhao Zhang, name: Jialiang Zhang, name: Chengyang Zhao, name: Haoran Lu, name: Yufei Ding, name: Ran Gong, name: Yuran Wang, name: Yuxuan Kuang, name: Ruihai Wu, name: Baoxiong Jia, name: Carlo Sferrazza, name: Hao Dong, name: Siyuan Huang, name: Yue Wang, name: Jitendra Malik, name: Pieter Abbeel
Published: 2025-04-26 12:31:04

Data scaling and standardized evaluation benchmarks have driven significant
advances in natural language processing and computer vision. However, robotics
faces unique challenges in scaling data and establishing evaluation protocols.
Collecting real-world data is resource-intensive and inefficient, while
benchmarking in real-world scenarios remains highly complex. Synthetic data and
simulation offer promising alternatives, yet existing efforts often fall short
in data quality, diversity, and benchmark standardization. To address these
challenges, we introduce RoboVerse, a comprehensive framework comprising a
simulation platform, a synthetic dataset, and unified benchmarks. Our
simulation platform supports multiple simulators and robotic embodiments,
enabling seamless transitions between different environments. The synthetic
dataset, featuring high-fidelity physics and photorealistic rendering, is
constructed through multiple approaches. Additionally, we propose unified
benchmarks for imitation learning and reinforcement learning, enabling
evaluation across different levels of generalization. At the core of the
simulation platform is MetaSim, an infrastructure that abstracts diverse
simulation environments into a universal interface. It restructures existing
simulation environments into a simulator-agnostic configuration system, as well
as an API aligning different simulator functionalities, such as launching
simulation environments, loading assets with initial states, stepping the
physics engine, etc. This abstraction ensures interoperability and
extensibility. Comprehensive experiments demonstrate that RoboVerse enhances
the performance of imitation learning, reinforcement learning, world model
learning, and sim-to-real transfer. These results validate the reliability of
our dataset and benchmarks, establishing RoboVerse as a robust solution for
advancing robot learning.
Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in
  Large Language Models against Reference Corruption
Authors: name: Wenxiao Wang, name: Parsa Hosseini, name: Soheil Feizi
Published: 2025-04-29 13:50:05

Chain-of-thought prompting has demonstrated great success in facilitating the
reasoning abilities of large language models. In this work, we explore how
these enhanced reasoning abilities can be exploited to improve the robustness
of large language models in tasks that are not necessarily reasoning-focused.
In particular, we show how a wide range of large language models exhibit
significantly improved robustness against reference corruption using a simple
method called chain-of-defensive-thought, where only a few exemplars with
structured and defensive reasoning are provided as demonstrations. Empirically,
the improvements can be astounding, especially given the simplicity and
applicability of the method. For example, in the Natural Questions task, the
accuracy of GPT-4o degrades from 60% to as low as 3% with standard prompting
when 1 out of 10 references provided is corrupted with prompt injection
attacks. In contrast, GPT-4o using chain-of-defensive-thought prompting
maintains an accuracy of 50%.
LawFlow : Collecting and Simulating Lawyers' Thought Processes
Authors: name: Debarati Das, name: Khanh Chi Le, name: Ritik Sachin Parkar, name: Karin De Langis, name: Brendan Madson, name: Chad M. Berryman, name: Robin M. Willis, name: Daniel H. Moses, name: Brett McDonnell, name: Daniel Schwarcz, name: Dongyeop Kang
Published: 2025-04-26 15:01:55

Legal practitioners, particularly those early in their careers, face complex,
high-stakes tasks that require adaptive, context-sensitive reasoning. While AI
holds promise in supporting legal work, current datasets and models are
narrowly focused on isolated subtasks and fail to capture the end-to-end
decision-making required in real-world practice. To address this gap, we
introduce LawFlow, a dataset of complete end-to-end legal workflows collected
from trained law students, grounded in real-world business entity formation
scenarios. Unlike prior datasets focused on input-output pairs or linear chains
of thought, LawFlow captures dynamic, modular, and iterative reasoning
processes that reflect the ambiguity, revision, and client-adaptive strategies
of legal practice. Using LawFlow, we compare human and LLM-generated workflows,
revealing systematic differences in structure, reasoning flexibility, and plan
execution. Human workflows tend to be modular and adaptive, while LLM workflows
are more sequential, exhaustive, and less sensitive to downstream implications.
Our findings also suggest that legal professionals prefer AI to carry out
supportive roles, such as brainstorming, identifying blind spots, and surfacing
alternatives, rather than executing complex workflows end-to-end. Building on
these findings, we propose a set of design suggestions, rooted in empirical
observations, that align AI assistance with human goals of clarity,
completeness, creativity, and efficiency, through hybrid planning, adaptive
execution, and decision-point support. Our results highlight both the current
limitations of LLMs in supporting complex legal workflows and opportunities for
developing more collaborative, reasoning-aware legal AI systems. All data and
code are available on our project page
(https://minnesotanlp.github.io/LawFlow-website/).
Learning Explainable Dense Reward Shapes via Bayesian Optimization
Authors: name: Ryan Koo, name: Ian Yang, name: Vipul Raheja, name: Mingyi Hong, name: Kwang-Sung Jun, name: Dongyeop Kang
Published: 2025-04-22 21:09:33

Current reinforcement learning from human feedback (RLHF) pipelines for large
language model (LLM) alignment typically assign scalar rewards to sequences,
using the final token as a surrogate indicator for the quality of the entire
sequence. However, this leads to sparse feedback and suboptimal token-level
credit assignment. In this work, we frame reward shaping as an optimization
problem focused on token-level credit assignment. We propose a reward-shaping
function leveraging explainability methods such as SHAP and LIME to estimate
per-token rewards from the reward model. To learn parameters of this shaping
function, we employ a bilevel optimization framework that integrates Bayesian
Optimization and policy training to handle noise from the token reward
estimates. Our experiments show that achieving a better balance of token-level
reward attribution leads to performance improvements over baselines on
downstream tasks and finds an optimal policy faster during training.
Furthermore, we show theoretically that explainability methods that are feature
additive attribution functions maintain the optimal policy as the original
reward.
CaRL: Learning Scalable Planning Policies with Simple Rewards
Authors: name: Bernhard Jaeger, name: Daniel Dauner, name: Jens Beißwenger, name: Simon Gerstenecker, name: Kashyap Chitta, name: Andreas Geiger
Published: 2025-04-24 17:56:01

We investigate reinforcement learning (RL) for privileged planning in
autonomous driving. State-of-the-art approaches for this task are rule-based,
but these methods do not scale to the long tail. RL, on the other hand, is
scalable and does not suffer from compounding errors like imitation learning.
Contemporary RL approaches for driving use complex shaped rewards that sum
multiple individual rewards, \eg~progress, position, or orientation rewards. We
show that PPO fails to optimize a popular version of these rewards when the
mini-batch size is increased, which limits the scalability of these approaches.
Instead, we propose a new reward design based primarily on optimizing a single
intuitive reward term: route completion. Infractions are penalized by
terminating the episode or multiplicatively reducing route completion. We find
that PPO scales well with higher mini-batch sizes when trained with our simple
reward, even improving performance. Training with large mini-batch sizes
enables efficient scaling via distributed data parallelism. We scale PPO to
300M samples in CARLA and 500M samples in nuPlan with a single 8-GPU node. The
resulting model achieves 64 DS on the CARLA longest6 v2 benchmark,
outperforming other RL methods with more complex rewards by a large margin.
Requiring only minimal adaptations from its use in CARLA, the same method is
the best learning-based approach on nuPlan. It scores 91.3 in non-reactive and
90.6 in reactive traffic on the Val14 benchmark while being an order of
magnitude faster than prior work.
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn
  Reinforcement Learning
Authors: name: Zihan Wang, name: Kangrui Wang, name: Qineng Wang, name: Pingyue Zhang, name: Linjie Li, name: Zhengyuan Yang, name: Kefan Yu, name: Minh Nhat Nguyen, name: Licheng Liu, name: Eli Gottlieb, name: Monica Lam, name: Yiping Lu, name: Kyunghyun Cho, name: Jiajun Wu, name: Li Fei-Fei, name: Lijuan Wang, name: Yejin Choi, name: Manling Li
Published: 2025-04-24 17:57:08

Training large language models (LLMs) as interactive agents presents unique
challenges including long-horizon decision making and interacting with
stochastic environment feedback. While reinforcement learning (RL) has enabled
progress in static tasks, multi-turn agent RL training remains underexplored.
We propose StarPO (State-Thinking-Actions-Reward Policy Optimization), a
general framework for trajectory-level agent RL, and introduce RAGEN, a modular
system for training and evaluating LLM agents. Our study on three stylized
environments reveals three core findings. First, our agent RL training shows a
recurring mode of Echo Trap where reward variance cliffs and gradient spikes;
we address this with StarPO-S, a stabilized variant with trajectory filtering,
critic incorporation, and decoupled clipping. Second, we find the shaping of RL
rollouts would benefit from diverse initial states, medium interaction
granularity and more frequent sampling. Third, we show that without
fine-grained, reasoning-aware reward signals, agent reasoning hardly emerge
through multi-turn RL and they may show shallow strategies or hallucinated
thoughts. Code and environments are available at
https://github.com/RAGEN-AI/RAGEN.
A Review of 3D Object Detection with Vision-Language Models
Authors: name: Ranjan Sapkota, name: Konstantinos I Roumeliotis, name: Rahul Harsha Cheppally, name: Marco Flores Calero, name: Manoj Karkee
Published: 2025-04-25 23:27:26

This review provides a systematic analysis of comprehensive survey of 3D
object detection with vision-language models(VLMs) , a rapidly advancing area
at the intersection of 3D vision and multimodal AI. By examining over 100
research papers, we provide the first systematic analysis dedicated to 3D
object detection with vision-language models. We begin by outlining the unique
challenges of 3D object detection with vision-language models, emphasizing
differences from 2D detection in spatial reasoning and data complexity.
Traditional approaches using point clouds and voxel grids are compared to
modern vision-language frameworks like CLIP and 3D LLMs, which enable
open-vocabulary detection and zero-shot generalization. We review key
architectures, pretraining strategies, and prompt engineering methods that
align textual and 3D features for effective 3D object detection with
vision-language models. Visualization examples and evaluation benchmarks are
discussed to illustrate performance and behavior. Finally, we highlight current
challenges, such as limited 3D-language datasets and computational demands, and
propose future research directions to advance 3D object detection with
vision-language models. >Object Detection, Vision-Language Models, Agents,
VLMs, LLMs, AI