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SkillsMP has collected 1,228 skills from ADu2021/skillXiv. Open a skill to review its source and details.
ADu2021/skillXivShowing 40 of 1,228 collected skills.
Convert video generation model outputs into executable robotic manipulation by extracting 3D object flow trajectories as an intermediate representation. Enables zero-shot manipulation of diverse object types (rigid, articulated, deformable, granular) without…
Scale agent learning by synthesizing diverse experiences using reasoning-based models instead of costly real-world rollouts, maintaining replay buffers with both real and synthetic interactions while using adaptive curriculum to focus on challenging tasks.
Unified system for multi-task video generation combining audio and visual synthesis, demonstrating scalable approaches for content generation that can enhance agent communication capabilities.
DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving. From arXiv:2601.01528
Implement techniques from DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal. Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial…
Improve LLM reasoning by promoting diversity at both trajectory and token levels simultaneously. Global (trajectory) scale rewards distinct correct solutions; local (token) scale applies entropy regularization per decision point. Dual-scale approach couples…
Implement techniques from DSGym: A Holistic Framework for Evaluating and Training Data Science Agents. Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findings
Optimize disaggregated prefill-decoding LLM serving for multi-turn (agentic) workloads by introducing dual-path KV-cache loading. Traditional approach loads all KV-cache to prefill engines, saturating their storage network. DualPath loads to decoding engines…
Generate SVGs through simultaneous image and SVG token generation with internal visual guidance. DuetSVG overcomes text-only limitations by leveraging visual predictions to enhance SVG coherence—ideal when visual quality and geometric correctness matter.
Implement dual preference optimization to generate self-supervised feedback without manual annotation by decomposing tasks into known/unknown components and reconstructing hidden information from model outputs.
Accelerate test-time scaling for diffusion language models by identifying inconsistent tokens, selectively remask and regenerate only uncertain tokens, and aggregate across samples via voting. Achieve 5.5-22× speedup over standard iterative sampling with 6-8%…
Automatically construct compact, diverse action spaces for LLM reasoning through corpus-based estimation and submodular optimization—enabling efficient decision-making without manual specification or expensive exhaustive search.
Implement hierarchical language modeling that compresses variable-length token sequences into high-capacity semantic concepts, achieving +2.69% benchmark improvements while reducing inference FLOPs by reallocating compute to concept-level reasoning. Use for…
Minimal modification to SFT that dynamically rescales objectives by token probability. Rectifies implicit reward structure to improve generalization comparable to RL while maintaining SFT simplicity.
Sparse attention mechanism combining content-aware and position-aware sparse patterns through dynamic masking. Achieves 10x speedup while maintaining model quality on long-context benchmarks through hardware-friendly implementation.
Dynamic token pruning framework for VLMs that adapts compression to scene complexity through single-pass selection. Removes 92.6% of visual tokens while maintaining performance and enabling superior fine-tuning.
Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.
Extends text embedding models to perform listwise reranking through continued training on ranking objectives. Constructs listwise prompts from queries and top-K candidates, leveraging pseudo-relevance feedback while maintaining embedding model efficiency.…
Monitor token-wise entropy to adaptively allocate compute during inference. Branch into multiple paths at high-entropy tokens, reducing token generation by up to 65% while improving accuracy by up to 37% on reasoning tasks.
Train efficient planners for long-horizon agent tasks using homologous consensus filtering to generate synthetic plans from strong LLMs and rule-based RL with executor capability rewards. Reduces training cost by 8x while maintaining state-of-the-art…
Enable image editors to handle complex instructions through iterative critique and refinement cycles. A multimodal LLM critiques editing results, reasons about improvements, and refines instructions until satisfactory output—ideal for instruction-following…
Systematically optimize agent system costs via empirical analysis of LLM, planning, memory, and search components achieving 28.4% cost reduction.
Systematically convert pretrained autoregressive models into efficient diffusion language models via block-wise attention and position-dependent masking. Efficient-DLM family (1.5B/4B/8B) maintains comparable accuracy to standard AR models while delivering…
Achieve 10× higher decoding throughput on long prompts by replacing 50% of cross-attention layers with gated memory units (GMUs) combining SSMs and attention. Maintains reasoning capability while reducing memory I/O bottleneck from O(d_kv·N) to O(d_h).
Framework for efficient machine unlearning that reformulates forgetting as inverse learning. Achieves significant computational speedup by replacing expensive Hessian operations with gradient-based optimization, enabling privacy-preserving model updates.
Comprehensive survey of techniques for optimizing large reasoning models. Covers single-model optimization and multi-model collaboration approaches to reduce reasoning path length without sacrificing capability.
Enable autonomous embodied agents to function in 3D communities with structured memory systems. Combines semantic memory (scene graphs, knowledge graphs) and episodic memory (spatiotemporal experiences) for social intelligence and multi-agent coordination.
Improve multimodal embeddings through RL-optimized reasoning that grounds evidence in retrievable visual cues. Frozen embedder provides stable rewards while reasoner generates evidential traceability CoT with text keywords, bounding boxes, and key frames.
Accelerate LLM decoding by predicting multiple future tokens simultaneously using mask-token probing in embedding space, without retraining or auxiliary models.
Bridge vision-to-action gap using pointing as unified intermediate representation, enabling 56.2% success on manipulation tasks without task-specific fine-tuning.
Discover hierarchical temporal abstractions within autoregressive models via internal RL, enabling efficient exploration of sparse-reward tasks. Metacontroller learns abstract action sequences modifying residual streams, switching gates enable quasi-binary…
Build a single model handling multimodal understanding, generation, and editing tasks efficiently through token compression and intelligent component sharing. EMMA-4B surpasses larger models while reducing computational burden—ideal when you need unified…
Improve exploration in LLM-based agents through external memory-augmented RL with hybrid on/off-policy training. Agents generate exploration 'tips' (self-reflections) after trajectories, storing them in memory. During rollouts, policy samples between standard…
Choose optimal pretraining strategy for text encoders: pure MLM, pure CLM, or biphasic CLM-then-MLM training, with empirical guidance on performance across downstream tasks.
Implement techniques from Endless Terminals: Scaling RL Environments for Terminal Agents. Environments are the bottleneck for self-improving agents
Enable step-by-step reasoning in diffusion models through iterative latent state refinement. Condition diffusion on evolving thought states across multiple reasoning steps, grounded with textual supervision to prevent drift.
Enhanced language model pre-training methodology improving linguistic competence across languages, strengthening foundational capabilities for multilingual agent systems.
Train LLMs to disambiguate tool calls in enterprise settings where multiple similar APIs exist and parameters are incomplete. Generates synthetic multi-turn dialogues with realistic ambiguity to improve tool selection accuracy by 27+ percentage points.
Technique for efficient model adaptation that mitigates catastrophic forgetting during fine-tuning, enabling agents to learn new tasks while preserving existing capabilities.
One-line code modification augmenting RL advantage function with clipped entropy term to encourage exploratory reasoning chains while maintaining optimization stability.