メニュー
このリポジトリの skills
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。
Achieve efficient neural networks via self-supervised dynamic routing using Cosine Incompatibility Ratio (CIR). Ground gating decisions in geometric novelty rather than learned heuristics, enable per-sample/per-block binary routing via Gumbel-softmax,…
原文の言語: 英語
Enable open-source reasoning model development with a 100K-instance Long CoT Collection, scaling from 1K o1 seed samples through guided synthesis with GPT-4o, achieving 2-3× RL performance gains.
原文の言語: 英語
Enable vision-language models to perform embodied question answering in 3D environments through active camera exploration. CoV uses training-free test-time reasoning to iteratively select relevant viewpoints and adjust camera angles until sufficient context…
原文の言語: 英語
CoVe synthesizes high-quality tool-use training data using explicit task constraints as both generation guidance and verification validators, enabling effective agent training without manual curation.
原文の言語: 英語
Enhance language model reasoning through coupled sampling from prior (question-only) and posterior (answer-conditioned) distributions. Construct composite distribution mixing both at token level using hybrid sampling. Combine reconstruction term, selective…
原文の言語: 英語
Optimize approximate nearest neighbor search via contrastive RL, learning to generate efficient code for HNSW graph construction, search, and refinement.
原文の言語: 英語
Permanently remove unwanted concepts from LLMs by identifying and suppressing sparse autoencoder features across layers, creating parameter-level changes that prevent reversal.
原文の言語: 英語
Improve formal theorem proofs by treating criticism—evaluation of semantic correctness—as a learning signal. Train critic models to distinguish correct from incorrect formalizations, then use their feedback to guide RL-based proof generation.
原文の言語: 英語
Improve LLM reasoning by combining numerical and natural language critique feedback in online RL for policy refinement.
原文の言語: 英語
Trains language models to provide quality feedback through two-stage RL. Stage 1 optimizes discriminability (distinguishing good vs bad responses). Stage 2 adds helpfulness rewards (improving actor after feedback). Achieves 9.02% improvement without requiring…
原文の言語: 英語
Create a universal memory infrastructure enabling agents across different frameworks to share experience trajectories without retraining. Improve agent performance by retrieving workflows from related domains and applying diagnostic fixes.
原文の言語: 英語
Build desktop agents via reusable, parameterized skills encoding human computer-use knowledge. Skills combine execution graphs (handling UI variations) with composition graphs (chaining strategies). 57.5% success on WindowsAgentArena.
原文の言語: 英語
Uses LLMs with RL to automatically optimize HGEMM CUDA kernels across 1,000 configurations, systematically outperforming NVIDIA's cuBLAS and cuBLASLt through continued pretraining, general RL, and specialized HGEMM RL stages.
原文の言語: 英語
Improve code and test generation through co-evolution where LLMs generate both solutions and tests, optimizing each based on mutual evaluation and discriminative testing performance.
原文の言語: 英語
While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We…
原文の言語: 英語
Improves reasoning efficiency through curriculum learning that progressively constrains token budgets, enabling models to first discover solution strategies then distill them into concise traces.
原文の言語: 英語
Framework for training cybersecurity agents without access to live environments. Uses CTF writeups and persona-driven LLM simulation to synthesize training trajectories, achieving performance matching proprietary systems like Claude-3.5-Sonnet.
原文の言語: 英語
Mitigate lazy reasoning in Large Reasoning Models via self-distillation teaching task decomposition, followed by Diversity-Aware GRPO with entropy-based advantage functions, enabling effective decomposition without external teachers while balancing structured…
原文の言語: 英語
Enable autonomous agent self-improvement through evolutionary mutation of agent codebases, using LLM-generated variants and empirical validation to discover beneficial modifications like enhanced tools and context management.
原文の言語: 英語
Accelerate the Shampoo optimizer 4.8x using batched block-wise preconditioning and numerical approximations, enabling more frequent preconditioner updates without computational bottleneck.
原文の言語: 英語
Boost language model performance by strategically ordering training data without changing content or model size. Uses learnability-quality scoring and folding schedules to improve convergence and knowledge retention, achieving consistent gains across all…
原文の言語: 英語
Train efficient robot manipulation policies by strategically applying task diversity and debiasing expert demonstrations to remove execution speed variations that degrade learning.
原文の言語: 英語
Automatically synthesize and optimize training data using GRPO to generate data recipes (specifications for dataset creation). Use a Data Verifier to efficiently evaluate sample quality without full model training. Achieve performance comparable to human…
原文の言語: 英語
Accelerate video generation by 14.8x through deep compression autoencoder (32x-64x spatial, 4x temporal compression) combined with lightweight adapter-based model adaptation. Use when deploying video diffusion models under compute or latency constraints.
原文の言語: 英語
Accelerate video generation through dual-expert consistency distillation, using separate denoisers for semantic layout/motion and detail refinement to resolve conflicting optimization gradients.
原文の言語: 英語
DCPO eliminates zero-gradient dead zones in policy optimization by adaptively adjusting token-level clipping bounds based on prior probabilities and smoothing advantage standardization across cumulative training steps, achieving 28% improvement in effective…
原文の言語: 英語
Accelerate diffusion transformer inference by dynamically adjusting patch granularity during generation based on detail complexity at each timestep. Early denoising steps (establishing low-frequency structure) use coarse patches; later steps (adding…
原文の言語: 英語
Bridge the gap between informal mathematical reasoning (80% accuracy) and formal proof synthesis (8% success) by decoupling them: a general-purpose reasoner generates strategic lemmas, then a specialized prover verifies them formally. First open-source solver…
原文の言語: 英語
Enables autonomous reasoning agents to discover and invoke tools efficiently through end-to-end training. Uses autonomous memory folding to compress interaction history and ToolPO to learn general-purpose tool use, applicable across diverse benchmarks from QA…
原文の言語: 英語
Maintains half of sliding window as attention sinks with dynamic temporal RoPE alignment plus importance-aware KV cache pruning, enabling 12× extrapolation beyond training length (60+ seconds from 5-second training) without fine-tuning.
原文の言語: 英語
Enhances model safety by filtering dual-use topics from pretraining data, creating tamper-resistant models robust to adversarial fine-tuning without degrading unrelated capabilities.
原文の言語: 英語
Reduce redundant tokens in parallel reasoning by 80% while maintaining accuracy via dynamic pruning of equivalent reasoning paths. Trigger: improve efficiency of consensus-based reasoning (multiple CoT generation).
原文の言語: 英語
DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation. From arXiv:2601.09688
原文の言語: 英語
Monitor search agent reasoning quality via hierarchical uncertainty detection. Fast consistency checks identify anomalies; slow experience-driven feedback provides corrections. Minimal overhead while catching misalignment.
原文の言語: 英語
Overcome exploration bottlenecks in reasoning RL by integrating Monte Carlo Tree Search during training (not just inference). Global frontier selection and entropy-guided sampling reduce GPU hours by 5.7x while improving performance.
原文の言語: 英語
Build research agents that systematically search for comprehensive answers to complex questions by maintaining search state, iterating on queries, and validating answer completeness. Implement strategies for identifying knowledge gaps and conducting follow-up…
原文の言語: 英語
Transform research specifications into production-grade codebases through strategic information management and autonomous agent orchestration. DeepCode surpasses PhD experts and commercial tools—critical when you need scientific code reproducibility at scale.
原文の言語: 英語
Filter low-quality reasoning traces using model-internal confidence signals at test time, eliminating weak paths during generation to achieve 99.9% accuracy while reducing token generation by up to 84.7%.
原文の言語: 英語
Train multimodal agents to dynamically invoke tools (code execution, web search) within reasoning loops through a two-stage pipeline combining cold-start supervised learning with reinforcement learning—enabling task-adaptive tool invocation for perception,…
原文の言語: 英語
Synergistic verifier-generator training loop enabling LLMs to identify logical issues in mathematical proofs without reference solutions, improving reasoning rigor through meta-verification. Apply when you need to scale mathematical reasoning without…
原文の言語: 英語