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SkillsMP は ADu2021/skillXiv から 1,228 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
ADu2021/skillXiv収集済み skill 1,228 件中 40 件を表示しています。
VCRL improves LLM mathematical reasoning by dynamically adjusting training sample difficulty based on group reward variance. Uses variance-based sampling with memory replay to focus on moderately-difficult samples where models succeed ~50% of the time,…
原文の言語: 英語
Validate LLM multi-step reasoning chains by converting natural language steps to first-order logic and checking logical consistency against established facts and sourced premises—enabling error detection and self-correction for more reliable reasoning.
原文の言語: 英語
Use model confidence and prediction consistency to select high-quality actions at test time without external verifiers, enabling reliable action selection in VLA systems.
原文の言語: 英語
Generate safety policies as executable code with formal verification, enabling provably-correct agent behavior through offline policy generation and online runtime enforcement.
原文の言語: 英語
Train agents to leverage external tools across domains using VerlTool's unified RL framework. Coordinate code execution, search, SQL queries, and vision utilities in multi-turn interactions without domain-specific redesign. 2× faster asynchronous rollouts on…
原文の言語: 英語
Enable parameter-efficient computation through dual-pathway feed-forward networks. Create virtual experts via hidden subspace slicing (width-versatile) and recursive weight application (depth-versatile). Use difficulty-aware gating to balance pathways, adding…
原文の言語: 英語
Control video generation via 4D geometric representation combining static background point clouds and per-object 3D Gaussian trajectories. Enable category-agnostic control over camera and multi-object motion in realistic video synthesis.
原文の言語: 英語
Optimize vision-language models for document tasks via embedding visualization and clustering-guided data generation. Identify error-prone regions in visual space and synthetically augment training data targeting weak areas.
原文の言語: 英語
Stabilize off-policy RL training for LLMs by deriving principled importance weight reshaping from variational optimization. Instead of heuristic clamping, VESPO uses closed-form exponential weighting W^c1 * exp(c2*(1-W)) to suppress overweighted samples while…
原文の言語: 英語
Use frozen vision foundation models like DINOv2 and CLIP as image tokenizers for autoregressive generation. Region-adaptive quantization identifies semantically coherent areas and reduces redundancy. Achieves 256-token encoding (vs. 576), 3× AR model speedup,…
原文の言語: 英語
Implements video deep research for multi-hop reasoning combining video analysis, web search, and evidence synthesis. Evaluates workflow vs agentic paradigms with 100-sample benchmark across 6 semantic domains, revealing goal drift and long-horizon consistency…
原文の言語: 英語
Use pre-trained video generation models (VGMs) as efficient reward models by evaluating video quality directly in latent space at any denoising timestep, enabling process-reward learning across the entire generation trajectory without expensive VAE decoding…
原文の言語: 英語
Ground video reasoning in explicit visual evidence by highlighting timestamps, objects, and bounding boxes, making reasoning verifiable and improving accuracy through RL rewards for spatio-temporal alignment.
原文の言語: 英語
Train video QA models on 6K examples using pure RL instead of costly supervised fine-tuning, then adaptively sample video frames at inference by monitoring answer consensus across multiple reasoning trajectories.
原文の言語: 英語
Diagnose and improve temporal pattern recognition in video-language models using SpookyBench, which isolates temporal information from spatial cues.
原文の言語: 英語
Enables long-term consistent video generation through three-tier memory architecture combining working memory, geometry-grounded point clouds, and episodic keyframes.
原文の言語: 英語
Reduce inference latency and token cost in video question-answering by selectively invoking chain-of-thought reasoning. VideoAuto-R1 uses confidence-based early-exit to determine when explicit reasoning is necessary, achieving 3.3× response length reduction…
原文の言語: 英語
Generate minute-scale coherent videos using state-space models as evolving memory for scene dynamics. VideoSSM achieves linear computational complexity while reducing motion drift—ideal when temporal consistency matters across long video sequences.
原文の言語: 英語
Transform video generation models into robot manipulators by jointly predicting actions and future visual outcomes. VideoVLA demonstrates that imagining futures improves action reliability—ideal when you need generalizable robot learning from video…
原文の言語: 英語
Implement techniques from VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input…
原文の言語: 英語
Predict robot actions through a perception-prediction-action loop: forecast dynamic regions, depth, and semantic features from visual observations, then generate action sequences via diffusion transformers.
原文の言語: 英語
Transfer reasoning capabilities from text LLMs to visual domains using reinforcement learning, achieving human-level visual reasoning on complex benchmarks. 38B parameters match closed-source VLMs by optimizing cross-modal connector alignment and…
原文の言語: 英語
Build efficient vision-language models by distilling knowledge from frozen diffusion decoders and vision encoders. Achieve GPT-4o-level captioning with <$1000 training cost by leveraging pre-trained components. Use when you need high-quality vision-language…
原文の言語: 英語
Apply test-time register token injection to pre-trained Vision Transformers without retraining, eliminating high-norm outlier artifacts and improving attention map quality.
原文の言語: 英語
Enable Vision-Language Models to improve without human annotations through competitive multi-agent self-play games (Who-Is-the-Spy format) combined with Iterative Self-Play Policy Optimization. Use when annotation costs limit multimodal dataset scaling or…
原文の言語: 英語
Enable VLMs to self-improve from unlabeled images via dual-role framework—questioner generates challenging visual questions while reasoner answers them, trained jointly with GRPO using difficulty and diversity rewards.
原文の言語: 英語
Implement techniques from VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology. Accurate semantic segmentation for histopathology image is crucial for quantitative tissue analysis…
原文の言語: 英語
Improve LMM performance on high-resolution images by training models to ground reasoning on image regions through RL, learning spatial localization without requiring expensive grounding annotations.
原文の言語: 英語
Enable multimodal models to dynamically revisit and re-ground reasoning steps in images using point-and-copy mechanisms for better long-horizon reasoning.
原文の言語: 英語
Enhance VLA models with spatiotemporal awareness by embedding both 3D spatial coordinates and temporal sequences: predict actions that include temporal parameters (duration) alongside spatial movements, achieving 97.4% robotic manipulation success by…
原文の言語: 英語
Enhance embodied robot reasoning by integrating explicit chain-of-thought supervision with reinforcement learning from verifiable rewards (GRPO+RL). Use when improving robot decision-making for tasks requiring spatial reasoning and constraint satisfaction.
原文の言語: 英語
Fine-tune Vision-Language-Action models using learned world models as simulators, eliminating costly real-world or physics-simulation RL. Train robust robot policies in 400 steps via GRPO with model-generated verified rewards.
原文の言語: 英語
Rolls forward robot state using previously generated actions to condition predictions on estimated future states, paired with temporal-offset training augmentation. Achieve 2× speedup and 17.4× latency reduction in vision-language-action models without…
原文の言語: 英語
VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory. From arXiv:2601.08665
原文の言語: 英語
Reduces video diffusion training compute 2.92× through spatio-temporal sparse attention with layer-wise cyclic block partitioning. Maintains generation quality while enabling long-sequence video training. Use when training video models on memory-constrained…
原文の言語: 英語
Accelerate speculative decoding by pruning drafter vocabulary to high-frequency tokens. Achieves 16% speedup in memory-bound settings by eliminating unused vocabulary entries without retraining.
原文の言語: 英語
Generate spatially-coherent 3D point-cloud videos from single images using depth-fused diffusion with efficient world caching for infinite scene exploration.
原文の言語: 英語
Unify multimodal understanding, generation, and reconstruction using high-dimensional codebooks for semantic information. VQRAE achieves 100% codebook utilization at 1536 dimensions—ideal when you need a single tokenizer for vision-language tasks.
原文の言語: 英語
Enable precise motion control in video generation using dense point trajectories as latent space features. Wan-Move integrates with existing models without architecture changes—ideal when you need fine-grained scene control without auxiliary motion encoders.
原文の言語: 英語
Convert internet tutorial videos into executable UI action trajectories using inverse dynamics models trained on 600K synthetic screen transitions. Generate 53K high-quality demonstrations spanning 69 applications, achieving state-of-the-art 7B agent…
原文の言語: 英語