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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。
Learn to route requests across multiple LoRA adapters using RL-based router training with constant routing weights. Prevents weight collapse and ensures balanced contribution from all selected adapters during inference.
原文の言語: 英語
Perform robust, low-cost model updates via REPAIR: closed-loop feedback, dynamic memory management, and frequent knowledge fusion. Preserve non-target knowledge while achieving 10-30% improvements in editing accuracy. Enable sequential edits without…
原文の言語: 英語
Enable language models to dynamically assign continuous position values to tokens based on contextual relevance instead of fixed linear positions. Implement learnable SwiGLU module extracting position information, integrate with RoPE for end-to-end…
原文の言語: 英語
Training-free token importance metric using representation shifts in MLP layers, enabling efficient compression compatible with fused kernels.
原文の言語: 英語
Dual-scoring mechanism (Magnitude and Stability scores) enabling process-level rewards that penalize inefficient reasoning patterns like overthinking and backtracking without trained reward models. Improve reasoning efficiency in RL without additional…
原文の言語: 英語
Apply software release engineering practices to self-improving LLM agents to achieve stable evolution. AgentDevel uses implementation-blind critique, executable diagnosis, and flip-centered gating to prevent regression while enabling auditable improvement…
原文の言語: 英語
Extract and transfer reasoning abilities using sparse autoencoders (SAE-Tuning) on CoT-free data, achieving RL-equivalent performance at 2000x lower cost and 450x faster training.
原文の言語: 英語
Evaluate large reasoning models under stress by asking multiple problems simultaneously, revealing true multi-tasking capacity and context management. Use to identify robustness gaps invisible in single-problem evaluations and discriminate model capabilities…
原文の言語: 英語
Generative approach for creating complex dynamic scenes and content, supporting agent capabilities in understanding and reasoning about multi-agent environments.
原文の言語: 英語
Enable longer reasoning within bounded context through iterative refinement: generate solution → verify → compress → refine. Achieves +11% on AIME with lower per-call latency than naive chain-of-thought.
原文の言語: 英語
Train LLM agents to evolve by generating dual intrinsic feedback signals—numerical rewards for capability progress and language lessons for future reuse. Combines hindsight reflection with memory-augmented RL using SimUtil-UCB retrieval.
原文の言語: 英語
Convert pretrained non-recurrent language models into depth-recurrent variants through a curriculum of increasing recurrence—decoupling training compute from inference compute and improving performance at given inference budgets on reasoning tasks.
原文の言語: 英語
Develop sophisticated multimodal reasoning through text-centric cold-start initialization, prioritized advantage distillation, and staged RL refinement.
原文の言語: 英語
Control MLLM output characteristics at inference time using separate reward models for precision and recall, enabling dynamic trade-offs without retraining.
原文の言語: 英語
Improve multimodal LLMs on fine-grained visual reasoning tasks (e.g., reading transit maps) by decomposing training into stages: basic perception (VQA) -> simple reasoning -> complex spatial reasoning. Incorporates 'detail rewards' for intermediate visual…
原文の言語: 英語
Advanced computer vision technique for robust spatial understanding in complex scenes, supporting agent navigation and environmental reasoning capabilities.
原文の言語: 英語
Enable robot policies to self-improve through imagination using learned dynamics and value models without physical trial-and-error. Compositional world model separates concerns enabling 35-45% performance gains on contact-rich manipulation.
原文の言語: 英語
Enable simultaneous optimization of environment difficulty, policy, and reward model. System uses reward model evaluations to guide environment adaptation, creating positive feedback loop for scalable agent improvement.
原文の言語: 英語
Correct color distortion in nighttime photos by combining statistical gray-pixel detection with reinforcement learning parameter optimization. Achieves superior cross-camera generalization without extensive labeled nighttime training data through a hybrid…
原文の言語: 英語
Scale LLM training using RL on unlabeled pre-training corpora without human annotation. Derive reward signals directly from text segments to optimize both autoregressive generation and in-context reasoning across knowledge and mathematical domains.
原文の言語: 英語
Hybrid-policy optimization combining multiple importance sampling and exploration-based advantage functions. Prevents capability boundary collapse while maintaining verification rewards, enabling LLMs to exceed baseline boundaries.
原文の言語: 英語
Extend RL to open-ended tasks using structured rubrics as reward anchors, enabling fine-grained evaluation of subjective outputs without requiring binary correctness signals.
原文の言語: 英語
RLVR extends reasoning capabilities by proving answer-only rewards implicitly incentivize correct intermediate reasoning via the Logic Prior principle.
原文の言語: 英語
RL in vision-language models improves inference-time alignment between vision and reasoning in mid-to-late layers, not vision ability or reasoning separately. Strategic layer freezing enables diagnosis of which components contribute to RL gains.
原文の言語: 英語
Train reasoning models to discover diverse solution strategies through two-player RL that jointly optimizes abstraction generation and solution derivation. Use when exploring multiple reasoning approaches is preferable to single-strategy depth.
原文の言語: 英語
Train LLMs to discover domain-specific abstractions (concise representations of procedural and factual knowledge) through two-player RL. An abstraction generator proposes key insights, a solution generator uses them to solve problems, and both receive RL…
原文の言語: 英語
RLAD performs selective distillation during RL, using trust-region ratio objectives to balance exploration, exploitation, and teacher guidance for efficient reasoning model training.
原文の言語: 英語
Use reinforcement learning to identify reasoning-critical attention heads and apply selective KV cache compression, reducing cache by 20-50% while preserving reasoning quality. Maintains speedups up to 1.21x with minimal performance loss.
原文の言語: 英語
Improve reasoning during pretraining (not just post-training) by computing rewards from information gain—how much reasoning improves log-likelihood of observed tokens. Works at 1T token scale across diverse corpora.
原文の言語: 英語
Integrate reinforcement learning into the pretraining phase by measuring the utility of intermediate reasoning for predicting subsequent tokens. This approach generates dense reward signals during standard pretraining, enabling models to develop reasoning…
原文の言語: 英語
Scale RL training for language models by automatically adapting problem difficulty to match model capabilities using verifiable reward environments—achieving 3.37% absolute improvement on reasoning benchmarks through environment diversity rather than compute…
原文の言語: 英語
Overcome reward function design challenges via General Reward Model (GRM) for step-wise progress assessment. Uses multi-view observations for occlusion robustness, hop-based progress normalization, Policy-Invariant Reward Shaping—enabling 95% robot task…
原文の言語: 英語
Brain-inspired framework integrating spatial, temporal, episodic, and semantic memory systems for embodied agents. Achieves 26.5% performance improvement via dynamic spatial knowledge graphs and closed-loop planning with critic modules.
原文の言語: 英語
Generate physically plausible robot manipulation videos by jointly learning RGB generation, temporal depth prediction, and keypoint dynamics. Enables training manipulation policies on synthetic data with strong correlation to simulator performance.
原文の言語: 英語
Train vision-language models for robotic manipulation using RL to improve embodied reasoning about spatial relationships and movements, achieving 31% improvement on manipulation benchmarks.
原文の言語: 英語
Generate synthetic robot manipulation data via diffusion models using visual identity prompting from exemplar images. Improve multi-view temporal coherence and scalability for robot policy training without extensive real-world data collection.
原文の言語: 英語
Enable MLLMs to handle visually degraded images by explicitly reasoning about degradation types and severity. Trains models to perceive degradation parameters, analyze semantic impact, and reconstruct interpretations while adapting reasoning depth to…
原文の言語: 英語
Compress language models by formulating heterogeneous layer-wise allocation as a constrained knapsack problem. Measure per-layer compression error, solve via dynamic programming to minimize total error within target model size, and avoid pathological…
原文の言語: 英語
Advanced reasoning approach for optimizing inference efficiency through meta-cognitive planning, enabling agents to make better decisions with reduced computational overhead.
原文の言語: 英語
Train efficient 14B-parameter agents via GRPO with resample-on-correct rollout strategy and Python code execution, achieving state-of-the-art reasoning through autonomous exploration and iterative refinement
原文の言語: 英語