Orionnnn/hub-stats huggingface dataset:
DRIFT: Learning from Abundant User Dissatisfaction in Real-World
  Preference Learning
Authors: name: Yifan Wang, name: Bolian Li, name: Junlin Wu, name: Zhaoxuan Tan, name: Zheli Liu, name: Ruqi Zhang, name: Ananth Grama, name: Qingkai Zeng
Published: 2025-09-27 03:06:27

Real-world large language model deployments (e.g., conversational AI systems,
code generation assistants) naturally generate abundant implicit user
dissatisfaction (DSAT) signals, as users iterate toward better answers through
refinements, corrections, and expressed preferences, while explicit
satisfaction (SAT) feedback is scarce. Existing preference learning approaches
are poorly aligned with this data profile, as they rely on costly human
annotations or assume plentiful positive responses. In this paper, we introduce
DRIFT (Dissatisfaction-Refined Iterative
preFerence Training), which anchors training on real-world
DSAT signals and samples positives dynamically from the evolving policy.
Empirically, DRIFT models trained on real-world WildFeedback datasets
and synthetic UltraFeedback datasets achieve up to +6.23\% (7B) /
+7.61\% (14B) on WildBench Task Score and up to +8.95\% (7B) / +12.29\% (14B)
on AlpacaEval2 win rate over base models, outperforming strong baseline methods
such as iterative DPO and SPIN. At larger scales, the improvements are
particularly pronounced: 14B models trained with DRIFT surpass GPT-4o-mini on
WildBench. Further analysis shows that DRIFT also preserves exploratory
capacity, yielding more diverse high-reward solutions rather than collapsing to
narrow subsets. Theoretically, we demonstrate that this design preserves
preference margins and avoids the gradient degeneration. These results show
that DRIFT is an effective and scalable recipe for real-world post-training
that leverages the most abundant and informative signal. The code and data are
available at https://github.com/cacayaya/DRIFT.git.
A Contextual Quality Reward Model for Reliable and Efficient Best-of-N
  Sampling
Authors: name: Hyung Gyu Rho
Published: 2025-10-05 08:23:08

Modern preference alignment techniques, such as Best-of-N (BoN) sampling,
rely on reward models trained with pairwise comparison data. While effective at
learning relative preferences, this paradigm fails to capture a signal of
response acceptability, leaving systems vulnerable to selecting the least bad
of many unacceptable options. This is particularly problematic for hard
prompts, where the risk of such false acceptances increases with the number of
samples. In this paper, we address this critical reliability gap by introducing
a new data collection and modeling framework. By augmenting preference data
with an outside option, inspired by discrete choice models, we train a reward
model that can distinguish not just what is better, but what is
good enough. We leverage this capability to create an adaptive
inference strategy, best of mini-N in-loop, which partitions the generation
budget into sequential loops with a calibrated, early-exit condition. Our
experiments show that when tuned as an alignment guardrail, it reduces
reliability failures by 70\%, and when tuned as an inference accelerator, it
improves average inference speed by over 22\% in IMDB-sentiment setting. We
thus provide a principled and flexible framework for practitioners to
explicitly manage the trade-off between reliability and computational
efficiency.
CoDA: Coding LM via Diffusion Adaptation
Authors: name: Haolin Chen, name: Shiyu Wang, name: Can Qin, name: Bo Pang, name: Zuxin Liu, name: Jielin Qiu, name: Jianguo Zhang, name: Yingbo Zhou, name: Zeyuan Chen, name: Ran Xu, name: Shelby Heinecke, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang, name: Weiran Yao
Published: 2025-09-27 05:41:55

Diffusion language models promise bidirectional context and infilling
capabilities that autoregressive coders lack, yet practical systems remain
heavyweight. We introduce CoDA, a 1.7B-parameter diffusion coder trained on TPU
with a fully open-source training pipeline. CoDA pairs large-scale diffusion
pre-training with code-centric mid-training and instruction tuning, enabling
confidence-guided sampling that keeps inference latency competitive. On
Humaneval, MBPP, and EvalPlus, CoDA-1.7B-Instruct matches or surpasses
diffusion models up to 7B parameters. Our release includes model checkpoints,
evaluation harnesses, and TPU training pipelines to accelerate research on
lightweight diffusion-based coding assistants.
Fast-dLLM v2: Efficient Block-Diffusion LLM
Authors: name: Chengyue Wu, name: Hao Zhang, name: Shuchen Xue, name: Shizhe Diao, name: Yonggan Fu, name: Zhijian Liu, name: Pavlo Molchanov, name: Ping Luo, name: Song Han, name: Enze Xie
Published: 2025-09-30 14:40:18

Autoregressive (AR) large language models (LLMs) have achieved remarkable
performance across a wide range of natural language tasks, yet their inherent
sequential decoding limits inference efficiency. In this work, we propose
Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that
efficiently adapts pretrained AR models into dLLMs for parallel text
generation, requiring only approximately 1B tokens of fine-tuning. This
represents a 500x reduction in training data compared to full-attention
diffusion LLMs such as Dream (580B tokens), while preserving the original
model's performance. Our approach introduces a novel training recipe that
combines a block diffusion mechanism with a complementary attention mask,
enabling blockwise bidirectional context modeling without sacrificing AR
training objectives. To further accelerate decoding, we design a hierarchical
caching mechanism: a block-level cache that stores historical context
representations across blocks, and a sub-block cache that enables efficient
parallel generation within partially decoded blocks. Coupled with our parallel
decoding pipeline, Fast-dLLM v2 achieves up to 2.5x speedup over standard AR
decoding without compromising generation quality. Extensive experiments across
diverse benchmarks demonstrate that Fast-dLLM v2 matches or surpasses AR
baselines in accuracy, while delivering state-of-the-art efficiency among dLLMs
- marking a significant step toward the practical deployment of fast and
accurate LLMs. Code and model will be publicly released.
EgoNight: Towards Egocentric Vision Understanding at Night with a
  Challenging Benchmark
Authors: name: Deheng Zhang, name: Yuqian Fu, name: Runyi Yang, name: Yang Miao, name: Tianwen Qian, name: Xu Zheng, name: Guolei Sun, name: Ajad Chhatkuli, name: Xuanjing Huang, name: Yu-Gang Jiang, name: Luc Van Gool, name: Danda Pani Paudel
Published: 2025-10-07 17:59:47

Most existing benchmarks for egocentric vision understanding focus primarily
on daytime scenarios, overlooking the low-light conditions that are inevitable
in real-world applications. To investigate this gap, we present EgoNight, the
first comprehensive benchmark for nighttime egocentric vision, with visual
question answering (VQA) as the core task. A key feature of EgoNight is the
introduction of day-night aligned videos, which enhance night annotation
quality using the daytime data and reveal clear performance gaps between
lighting conditions. To achieve this, we collect both synthetic videos rendered
by Blender and real-world recordings, ensuring that scenes and actions are
visually and temporally aligned. Leveraging these paired videos, we construct
EgoNight-VQA, supported by a novel day-augmented night auto-labeling engine and
refinement through extensive human verification. Each QA pair is double-checked
by annotators for reliability. In total, EgoNight-VQA contains 3658 QA pairs
across 90 videos, spanning 12 diverse QA types, with more than 300 hours of
human work. Evaluations of state-of-the-art multimodal large language models
(MLLMs) reveal substantial performance drops when transferring from day to
night, underscoring the challenges of reasoning under low-light conditions.
Beyond VQA, EgoNight also introduces two auxiliary tasks, day-night
correspondence retrieval and egocentric depth estimation at night, that further
explore the boundaries of existing models. We believe EgoNight-VQA provides a
strong foundation for advancing application-driven egocentric vision research
and for developing models that generalize across illumination domains. All the
data and code will be made available upon acceptance.
Deforming Videos to Masks: Flow Matching for Referring Video
  Segmentation
Authors: name: Zanyi Wang, name: Dengyang Jiang, name: Liuzhuozheng Li, name: Sizhe Dang, name: Chengzu Li, name: Harry Yang, name: Guang Dai, name: Mengmeng Wang, name: Jingdong Wang
Published: 2025-10-07 17:14:10

Referring Video Object Segmentation (RVOS) requires segmenting specific
objects in a video guided by a natural language description. The core challenge
of RVOS is to anchor abstract linguistic concepts onto a specific set of pixels
and continuously segment them through the complex dynamics of a video. Faced
with this difficulty, prior work has often decomposed the task into a pragmatic
`locate-then-segment' pipeline. However, this cascaded design creates an
information bottleneck by simplifying semantics into coarse geometric prompts
(e.g, point), and struggles to maintain temporal consistency as the segmenting
process is often decoupled from the initial language grounding. To overcome
these fundamental limitations, we propose FlowRVS, a novel framework that
reconceptualizes RVOS as a conditional continuous flow problem. This allows us
to harness the inherent strengths of pretrained T2V models, fine-grained pixel
control, text-video semantic alignment, and temporal coherence. Instead of
conventional generating from noise to mask or directly predicting mask, we
reformulate the task by learning a direct, language-guided deformation from a
video's holistic representation to its target mask. Our one-stage, generative
approach achieves new state-of-the-art results across all major RVOS
benchmarks. Specifically, achieving a J&F of 51.1 in
MeViS (+1.6 over prior SOTA) and 73.3 in the zero shot Ref-DAVIS17 (+2.7),
demonstrating the significant potential of modeling video understanding tasks
as continuous deformation processes.
Margin Adaptive DPO: Leveraging Reward Model for Granular Control in
  Preference Optimization
Authors: name: Hyung Gyu Rho
Published: 2025-10-06 20:09:37

Direct Preference Optimization (DPO) has emerged as a simple and effective
method for aligning large language models. However, its reliance on a fixed
temperature parameter leads to suboptimal training on diverse preference data,
causing overfitting on easy examples and under-learning from informative ones.
Recent methods have emerged to counter this. While IPO addresses general
overfitting, its uniform regularization can be overly conservative. The more
targeted approach of beta-DPO suffers from its own limitations: its
batch-level adaptation applies a single, compromised temperature to
mixed-margin pairs, its linear update rule can produce unstable negative
beta values, and its filtering mechanism discards potentially useful
training signals. In this work, we introduce Margin-Adaptive Direct Preference
Optimization (MADPO), a method that provides a stable, data-preserving, and
instance-level solution. MADPO employs a practical two-step approach: it first
trains a reward model to estimate preference margins and then uses these
margins to apply a continuous, adaptive weight to the DPO loss for each
individual training sample. This re-weighting scheme creates an effective
target margin that is amplified for hard pairs and dampened for easy pairs,
allowing for granular control over the learning signal. We provide a
comprehensive theoretical analysis, proving that MADPO has a well-behaved
optimization landscape and is robust to reward model estimation errors. We
validate our theory with experiments on a sentiment generation task, where
MADPO consistently and significantly outperforms strong baselines across
datasets of varying quality. It achieves performance gains of up to +33.3\% on
High Quality data and +10.5\% on Low Quality data over the next-best method.
Our results establish MADPO as a more robust and principled approach to
preference alignment.
AInstein: Assessing the Feasibility of AI-Generated Approaches to
  Research Problems
Authors: name: Shambhavi Mishra, name: Gaurav Sahu, name: Marco Pedersoli, name: Laurent Charlin, name: Jose Dolz, name: Christopher Pal
Published: 2025-10-06 22:50:41

Large language models (LLMs) demonstrate impressive capabilities across a
wide range of tasks, yet it remains unclear whether such success reflects
genuine reasoning or sophisticated recall. We introduce AInstein, a framework
for testing whether LLMs can generate valid solutions to AI research problems
using only their pretrained parametric knowledge -- without domain-specific
fine-tuning, retrieval augmentation, or other external aids. Our approach
extracts distilled problem statements from high-quality ICLR 2025 submissions,
then tasks specialized solver agents with proposing and refining technical
solutions through iterative critique loops, mimicking the cycles of proposal,
review, and revision central to scientific inquiry. We evaluate AInstein on
1,214 ICLR papers stratified by acceptance tier (Oral, Spotlight, Poster),
using an LLM-as-a-judge paradigm guided by a structured rubric, complemented by
targeted manual checks. Performance is assessed with three metrics: Success
Rate (does the solution address the problem?), Rediscovery (does it align with
human-proposed methods?), and Novelty (does it yield valid, original
approaches?). Our results reveal that while LLMs can rediscover feasible
solutions and occasionally propose creative alternatives, their problem-solving
ability remains fragile and highly sensitive to framing. These findings provide
the first large-scale evidence on the extent to which LLMs can act as
autonomous scientific problem-solvers, highlighting both their latent potential
and their current limitations.
Demystifying deep search: a holistic evaluation with hint-free multi-hop
  questions and factorised metrics
Authors: name: Maojia Song, name: Renhang Liu, name: Xinyu Wang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Soujanya Poria, name: Jingren Zhou
Published: 2025-10-01 07:59:03

RAG (Retrieval-Augmented Generation) systems and web agents are increasingly
evaluated on multi-hop deep search tasks, yet current practice suffers from two
major limitations. First, most benchmarks leak the reasoning path in the
question text, allowing models to follow surface cues rather than discover
reasoning chains autonomously. Second, evaluation is typically reduced to a
single pass rate, which collapses diverse behaviours into one score and
obscures whether failures stem from inadequate search, poor knowledge use, or
inappropriate refusal. To address these issues, we present WebDetective, a
benchmark of hint-free multi-hop questions paired with a controlled Wikipedia
sandbox that ensures full traceability of model actions, and a holistic
evaluation framework that separates search sufficiency, knowledge utilisation,
and refusal behaviour. Our evaluation of 25 state-of-the-art models reveals
systematic weaknesses across all architectures: models struggle with knowledge
utilisation despite having sufficient evidence and demonstrate near-absent
appropriate refusal when evidence is lacking. These patterns expose a
fundamental gap: today's systems excel at executing given reasoning paths but
fail when required to discover them. We develop an agentic workflow,
EvidenceLoop, that explicitly targets the challenges our benchmark identifies,
incorporating verification loops and systematic evidence tracking that improve
both search and synthesis capabilities. This baseline demonstrates that
WebDetective's diagnostic framework can guide concrete architectural
improvements, establishing our benchmark as a critical tool for developing
genuinely autonomous reasoning systems rather than pattern-following agents.
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular
  Reasoning
Authors: name: Jiaru Zou, name: Soumya Roy, name: Vinay Kumar Verma, name: Ziyi Wang, name: David Wipf, name: Pan Lu, name: Sumit Negi, name: James Zou, name: Jingrui He
Published: 2025-10-07 17:59:41

Process Reward Models (PRMs) have recently emerged as a powerful framework
for enhancing the reasoning capabilities of large reasoning models (LRMs),
particularly in the context of test-time scaling (TTS). However, their
potential for supervising LRMs on tabular reasoning domains remains
underexplored. Through detailed empirical analyses, we identify that existing
PRMs, though widely adopted for supervising text-only reasoning steps, struggle
with table-specific operations such as sub-table retrieval and schema
interaction, leading to critical performance bottlenecks. To address this
limitation, we propose TaTToo, a novel table-grounded PRM framework that (i)
reasons explicitly over tabular reasoning steps and (ii) integrates tool-based
verification to provide precise reward supervision. Concretely, we first design
a scalable data curation pipeline that constructs over 60k high-quality
step-level annotations by integrating table verification rationales with
tool-based executions. Building on the collected data, we train TaTToo with a
dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use
reasoning patterns, followed by reinforcement learning with tool-grounded
reward shaping to align our model with table-based verification. We provide a
comprehensive evaluation of the policy improvement induced by our newly
designed PRM. Across 5 challenging tabular reasoning benchmarks covering
numerical reasoning, fact-checking, and data analysis, TaTToo improves
downstream policy LRMs by 30.9% at inference, surpasses strong PRM baselines
such as Qwen-2.5-Math-PRM-72B with only 8B parameters, and demonstrates strong
generalizability across diverse TTS strategies.
Optimized Minimal 4D Gaussian Splatting
Authors: name: Minseo Lee, name: Byeonghyeon Lee, name: Lucas Yunkyu Lee, name: Eunsoo Lee, name: Sangmin Kim, name: Seunghyeon Song, name: Joo Chan Lee, name: Jong Hwan Ko, name: Jaesik Park, name: Eunbyung Park
Published: 2025-10-04 16:11:13

4D Gaussian Splatting has emerged as a new paradigm for dynamic scene
representation, enabling real-time rendering of scenes with complex motions.
However, it faces a major challenge of storage overhead, as millions of
Gaussians are required for high-fidelity reconstruction. While several studies
have attempted to alleviate this memory burden, they still face limitations in
compression ratio or visual quality. In this work, we present OMG4 (Optimized
Minimal 4D Gaussian Splatting), a framework that constructs a compact set of
salient Gaussians capable of faithfully representing 4D Gaussian models. Our
method progressively prunes Gaussians in three stages: (1) Gaussian Sampling to
identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning
to remove redundancies, and (3) Gaussian Merging to fuse primitives with
similar characteristics. In addition, we integrate implicit appearance
compression and generalize Sub-Vector Quantization (SVQ) to 4D representations,
further reducing storage while preserving quality. Extensive experiments on
standard benchmark datasets demonstrate that OMG4 significantly outperforms
recent state-of-the-art methods, reducing model sizes by over 60% while
maintaining reconstruction quality. These results position OMG4 as a
significant step forward in compact 4D scene representation, opening new
possibilities for a wide range of applications. Our source code is available at
https://minshirley.github.io/OMG4/.
Multilingual Routing in Mixture-of-Experts
Authors: name: Lucas Bandarkar, name: Chenyuan Yang, name: Mohsen Fayyaz, name: Junlin Hu, name: Nanyun Peng
Published: 2025-10-06 11:09:20

Mixture-of-Experts (MoE) architectures have become the key to scaling modern
LLMs, yet little is understood about how their sparse routing dynamics respond
to multilingual data. In this work, we analyze expert routing patterns using
parallel multilingual datasets and present highly interpretable layer-wise
phenomena. We find that MoE models route tokens in language-specific ways in
the early and late decoder layers but exhibit significant cross-lingual routing
alignment in middle layers, mirroring parameter-sharing trends observed in
dense LLMs. In particular, we reveal a clear, strong correlation between a
model's performance in a given language and how similarly its tokens are routed
to English in these layers. Extending beyond correlation, we explore
inference-time interventions that induce higher cross-lingual routing
alignment. We introduce a method that steers the router by promoting
middle-layer task experts frequently activated in English, and it successfully
increases multilingual performance. These 1-2% gains are remarkably consistent
across two evaluation tasks, three models, and 15+ languages, especially given
that these simple interventions override routers of extensively trained,
state-of-the-art LLMs. In comparison, interventions outside of the middle
layers or targeting multilingual-specialized experts only yield performance
degradation. Altogether, we present numerous findings that explain how MoEs
process non-English text and demonstrate that generalization is limited by the
model's ability to leverage language-universal experts in all languages.
CWM: An Open-Weights LLM for Research on Code Generation with World
  Models
Authors: name: FAIR CodeGen team, name: Quentin Carbonneaux, name: Gal Cohen, name: Jonas Gehring, name: Jacob Kahn, name: Jannik Kossen, name: Felix Kreuk, name: Emily McMilin, name: Michel Meyer, name: Yuxiang Wei, name: David Zhang, name: Kunhao Zheng, name: Jordi Armengol-Estapé, name: Pedram Bashiri, name: Maximilian Beck, name: Pierre Chambon, name: Abhishek Charnalia, name: Chris Cummins, name: Juliette Decugis, name: Zacharias V. Fisches, name: François Fleuret, name: Fabian Gloeckle, name: Alex Gu, name: Michael Hassid, name: Daniel Haziza, name: Badr Youbi Idrissi, name: Christian Keller, name: Rahul Kindi, name: Hugh Leather, name: Gallil Maimon, name: Aram Markosyan, name: Francisco Massa, name: Pierre-Emmanuel Mazaré, name: Vegard Mella, name: Naila Murray, name: Keyur Muzumdar, name: Peter O'Hearn, name: Matteo Pagliardini, name: Dmitrii Pedchenko, name: Tal Remez, name: Volker Seeker, name: Marco Selvi, name: Oren Sultan, name: Sida Wang, name: Luca Wehrstedt, name: Ori Yoran, name: Lingming Zhang, name: Taco Cohen, name: Yossi Adi, name: Gabriel Synnaeve
Published: 2025-09-30 21:47:10

We release Code World Model (CWM), a 32-billion-parameter open-weights LLM,
to advance research on code generation with world models. To improve code
understanding beyond what can be learned from training on static code alone, we
mid-train CWM on a large amount of observation-action trajectories from Python
interpreter and agentic Docker environments, and perform extensive multi-task
reasoning RL in verifiable coding, math, and multi-turn software engineering
environments. With CWM, we provide a strong testbed for researchers to explore
the opportunities world modeling affords for improving code generation with
reasoning and planning in computational environments. We present first steps of
how world models can benefit agentic coding, enable step-by-step simulation of
Python code execution, and show early results of how reasoning can benefit from
the latter. CWM is a dense, decoder-only LLM trained with a context size of up
to 131k tokens. Independent of its world modeling capabilities, CWM offers
strong performance on general coding and math tasks: it reaches pass@1 scores
of 65.8% on SWE-bench Verified (with test-time scaling), 68.6% on
LiveCodeBench, 96.6% on Math-500, and 76.0% on AIME 2024. To support further
research on code world modeling, we release model checkpoints after
mid-training, SFT, and RL.