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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.
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,…
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.
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…
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.
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…
Optimize approximate nearest neighbor search via contrastive RL, learning to generate efficient code for HNSW graph construction, search, and refinement.
Permanently remove unwanted concepts from LLMs by identifying and suppressing sparse autoencoder features across layers, creating parameter-level changes that prevent reversal.
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.
Improve LLM reasoning by combining numerical and natural language critique feedback in online RL for policy refinement.
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…
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.
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.
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.
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.
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…
Improves reasoning efficiency through curriculum learning that progressively constrains token budgets, enabling models to first discover solution strategies then distill them into concise traces.
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.
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…
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.
Accelerate the Shampoo optimizer 4.8x using batched block-wise preconditioning and numerical approximations, enabling more frequent preconditioner updates without computational bottleneck.
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…
Train efficient robot manipulation policies by strategically applying task diversity and debiasing expert demonstrations to remove execution speed variations that degrade learning.
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…
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.
Accelerate video generation through dual-expert consistency distillation, using separate denoisers for semantic layout/motion and detail refinement to resolve conflicting optimization gradients.
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…
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…
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…
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…
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.
Enhances model safety by filtering dual-use topics from pretraining data, creating tamper-resistant models robust to adversarial fine-tuning without degrading unrelated capabilities.
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).
DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation. From arXiv:2601.09688
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.
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.
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…
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.
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%.
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,…
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…