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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.
Run draft and target models in parallel across GPU/NPU using bidirectional speculation: draft predicts forward continuations while target speculates correction paths simultaneously. Achieve 2.8x-5.8x wall-time speedup on 14B-66B models.
Score reasoning paths using PMI to identify question-specific relevant steps without rollout simulations, enabling efficient tree search 12× faster than MCTS while improving reasoning accuracy.
Improve reasoning efficiency by dynamically adjusting elaboration depth within a single response. Difficult steps receive extensive chain-of-thought; straightforward steps use concise inference. Mix reasoning modes to achieve 57-79% token savings under…
Allow attention heads to reference features from multiple depths by accessing both current-layer and depth key-value pairs. Prevent signal degradation in deep models while maintaining computational efficiency.
Optimize Mixture-of-Experts efficiency by decoupling token-level expert activation from layer architecture. Use dynamic threshold routing where expert count per token varies by input complexity, and apply layer-wise capacity scheduling to match…
Enable LLMs to autonomously select and apply diverse reasoning strategies without prompt engineering. Trains models with diverse thought templates covering 50-500 distinct reasoning approaches, achieving 2-13% improvements over baseline prompting methods.
Build parameter-efficient models that assign different computation depths per token via learned routing, combining weight sharing with dynamic computation complexity. Use when you need to maximize model capacity within compute budgets or create models that…
Train multimodal models for long-chain reflective reasoning (iterative thinking, backtracking) using Adaptive Hybrid Policy Optimization. Trigger: improve VLM performance on tasks requiring iterative refinement and error correction.
Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench…
Evaluate whether generative models possess foundational reasoning capabilities. Develop five-ability framework (physical, logical, 3D spatial, 2D spatial, temporal reasoning) across abstract reasoning, embodied navigation, and physical commonsense benchmarks.…
Compresses MoE language models through shared basis factorization of expert weight matrices, achieving 24-30% parameter reduction with minimal accuracy loss.
Build GUI automation agents using self-evolving trajectory generation, trajectory-aware policy optimization, and integrated action semantics for cross-platform interaction.
Transform pre-trained vision-language models into powerful bidirectional multimodal embeddings through modality-aware continual pre-training and heterogeneous contrastive fine-tuning. 3B model matches 7B baselines.
Enable small vision-language models to reason over diverse data types by converting latent embeddings into visual representations, achieving specialized performance without domain-specific training.
Merges multiple fine-tuned models by operating in input-representation space rather than parameter space. Creates synthetic inputs whose gradients align with task vectors, bridging joint training and post-hoc merging for robust multi-task model combination.
Optimize data mixtures across diverse vision-language domains when applying RL with verifiable rewards to multimodal LLMs, balancing task-specific performance with generalization.
Train large models efficiently across heterogeneous hardware (GPUs, TPUs, Trainium) using strict encapsulation principles, achieving constant code complexity when adding features across hundreds of modules.
Prevents MoE router instability during RL training by recording and replaying inference-phase routing distributions back into training. Reduces training-inference routing divergence and KL divergence, enabling stable MoE RL scaling without sacrificing…
Determine optimal MoE sparsity by separating memorization and reasoning trade-offs: active FLOPs improve reasoning while total parameters improve memorization, requiring joint optimization
Improve agent reasoning by designing thought structures that balance deep analysis, self-reflection, and exploratory thinking. Framework discovers that effective long-form reasoning exhibits molecular-like interaction patterns—specific bonds between reasoning…
Enable multimodal LLMs to generate and reason with latent visual embeddings as intermediate thoughts: implement supervised fine-tuning to produce continuous visual representations, then optimize via VLPO to treat embeddings as learnable actions in…
Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.
Generate videos at 29 FPS with interactive motion control through teacher-student distillation of motion-conditioned video models, using sliding-window causal attention and attention sinks to maintain constant latency for indefinite-length generation.
Improve factuality in large reasoning models by analyzing reasoning state transitions and reweighting preference optimization signals, suppressing defective reasoning segments while amplifying patterns that lead to factual outputs.
Improve agent search through meta-RL: generate multiple episodes sequentially, each building on prior attempts with explicit self-reflection. Use turn-level RLOO advantage estimation to provide dense credit without value models.
Prevent unrecoverable gradient explosions in LLM training by periodically restoring weight matrix stable rank through SVD-based matrix sign operations, eliminating sudden training failures without computational burden.
Apply μ-parametrization to Mixture-of-Experts architectures to enable reliable hyperparameter transfer across model sizes, eliminating costly retuning when scaling to trillion-parameter systems.
Enables LLM self-improvement without external verification through multi-agent co-evolution. Proposer generates questions, Solver attempts solutions, Judge evaluates both. All agents evolve together via RL, achieving 4.54% improvement on reasoning benchmarks…
Design multi-agent systems with brain-inspired memory mechanisms that enable efficient information sharing and coordination. Implement hierarchical memory structures (working memory, episodic memory, semantic memory) similar to neuroscience models to improve…
Build persistent, structured memory systems for LLM agents that remember user context across sessions, organize information semantically, and retrieve relevant knowledge automatically before responding. Achieves 35% accuracy gains over RAG baselines with…
Train planner and worker agent roles within a single LLM via role-specific prompts and RL, avoiding multi-instance overhead while preserving specialization. Trigger: improve tool-use planning robustness to noisy outputs without deploying separate models.
Accelerate autoregressive image generation via multi-resolution drafting with spatially-informed verification. Local rejection and resampling enable efficient error correction focusing on spatial neighborhoods, achieving 1.7× speedup over baselines.
Enable balanced multi-task GRPO training via robustness-aware optimization and improvement-aware task reweighting, dynamically adjusting task weights based on both reward and loss trajectory improvement, achieving 6-28% worst-task improvements while…
Enable agents to communicate through shared latent thoughts rather than natural language, recovering both shared and private latent representations with theoretical guarantees for more efficient collaboration.
Train specialized agents in pipelines using dense per-action process rewards from AI coaching. Solves credit assignment in sequential workflows, enabling better generalization and faster convergence than outcome-only training.
Improve text-image alignment in diffusion transformers through Temperature-Adjusted Cross-modal Attention (TACA), addressing token imbalance and timestep-dependent weighting with parameter-efficient LoRA fine-tuning.
Generate unified embeddings for videos, images, and visual documents enabling semantic similarity, retrieval, and clustering across heterogeneous visual content types.
Enable native parallel token generation in language models by implementing adaptive task decomposition and merge strategies, achieving 2x speedup with 1.87% performance gains.
Improve LLM training efficiency by selectively applying Muon optimizer to Value-Output attention weights and FFN layers, which function as associative memories. Use when training data exhibits heavy-tailed distributions requiring robust rare-fact learning.
Enhanced language model pre-training methodology improving linguistic competence across languages, strengthening foundational capabilities for multilingual agent systems.