ADu2021/skillXiv - 13ページ

goedel-prover-formal-theorem-proving

Train language models for formal theorem proving via expert iteration with verifier-guided self-correction and checkpoint merging.

原文の言語: 英語

golden-goose-task-synthesis

Synthesize unlimited verifiable training tasks from unverifiable text by converting reasoning passages into multiple-choice problems. Creates higher-quality training data for RLHF systems without requiring new human labels.

原文の言語: 英語

gorl-generative-online-rl

Separates generative policy optimization through latent encoder (standard RL algorithms) and conditional decoder (frozen then refined), using two-timescale alternating schedule to eliminate gradient instability from direct generative policy optimization.

原文の言語: 英語

governed-autonomy-drug-discovery

Balance LLM flexibility with domain rigor in scientific agents through dual-layer architecture. Enforce role-based access control and artifact-centric state management to prevent hallucinations, while preserving free-form reasoning for lower-risk tasks.

原文の言語: 英語

gradient-grouping-learning-rate-scaling

Improve adaptive learning rates by clustering gradient statistics within layers and applying cluster-specific scaling.

原文の言語: 英語

gradmem-context-memory-compression

Compress long context into compact memory tokens via iterative gradient descent. Learn to write information into prefix memory without storing full KV-caches, enabling efficient long-context reasoning and retrieval.

原文の言語: 英語

grao-unified-alignment

Unifies supervised fine-tuning and reinforcement learning through GRAO framework that combines multiple-output generation with group direct alignment loss for improved preference learning.

原文の言語: 英語

grape-group-position-encoding

Unify positional encoding methods via group action theory, encompassing RoPE and ALiBi as special cases. GRAPE enables exploration of cross-subspace feature coupling—ideal when you need principled positional encoding beyond standard implementations.

原文の言語: 英語

graph-optimization-test-time-compute

Optimize test-time computation through graph-based collaborative architecture where nodes represent models, edges represent information flow, and topology itself is optimizable via reinforcement learning to discover ideal model assignments and configurations.

原文の言語: 英語

green-vla-robotics

Train robot controllers via five-stage curriculum progressing from base vision-language models to embodiment-specific RL-refined policies. Unified action space enables cross-embodiment transfer with minimal performance loss.

原文の言語: 英語

group-rank-reranking-rl

Improve ranking quality via groupwise reranking with RL—process document groups jointly for within-group comparisons using GRPO with composite rewards (recall, ranking metrics, distribution alignment).

原文の言語: 英語

grove-moe-adjugate-experts

Enables efficient MoE architectures through heterogeneous expert sizing and dynamic activation mechanisms that adjust parameter count based on input complexity.

原文の言語: 英語

growing-transformers-modular-expansion

Build language models layer-by-layer on frozen embeddings, training new Transformer blocks sequentially while keeping lower layers frozen. Achieves 5% improvement over monolithic baselines on MMLU while fitting 740M trainable parameters per stage on single…

原文の言語: 英語

grpo-ma-multi-answer-cot-training

Stabilize and accelerate chain-of-thought RL training by sampling multiple answers per generated thought. GRPO-MA reduces gradient noise and improves convergence across math, code, vision, and manipulation tasks while cutting computational cost versus naive…

原文の言語: 英語

gtr-turbo-vlm

Eliminate expensive external teacher dependencies in VLM RL training via merged-checkpoint teachers. Uses TIES merging of historical RL checkpoints to create free, stable teacher models for step-level guidance—matching external teacher performance while…

原文の言語: 英語

guardians-of-hair-soft-boundaries

Recover fine details at soft boundaries (hair, fur) through depth refinement networks and view synthesis. Integrate plug-and-play with existing depth models via adaptive combination across monocular, stereo, and novel view tasks.

原文の言語: 英語

gui-360-desktop-agent-dataset

Enable training and evaluation of desktop computer-using agents through 1.2M action steps across diverse Windows applications, covering GUI grounding, screen parsing, and action prediction with hybrid GUI+API action space reflecting modern agent designs.

原文の言語: 英語

gui-actor-grounding

Enable GUI agents to ground actions without generating pixel coordinates by using attention-based patch-level alignment and a verifier for selecting optimal action regions from candidates.

原文の言語: 英語

gui-test-time-scaling

Improve GUI agent planning and action grounding through test-time scaling and reinforcement learning. Sample and evaluate multiple action candidates, then use RL to precisely target visual interface elements.

原文の言語: 英語

guidelines-to-prompt-large-language-models-for-cod

Implement techniques from Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization. Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation

原文の言語: 英語

h-net-dynamic-chunking-hierarchical-sequence

Eliminate fixed tokenization by learning data-dependent segmentation jointly with the model through dynamic chunking, matching BPE-tokenized Transformers at equivalent compute while showing improved robustness and better downstream task performance without…

原文の言語: 英語

hardtests-code-verification

Generate comprehensive test cases for code problems that reliably detect wrong solutions through LLM-based edge case synthesis and test quality ranking.

原文の言語: 英語

hardware-agnostic-reranker-evaluation

Evaluate LLM-based document rerankers using hardware-agnostic FLOPs metrics instead of latency, enabling fair comparison of ranking quality per unit of computation across different models and deployment scenarios.

原文の言語: 英語

harmonyguard-safety-utility-agents

Multi-agent framework that balances safety compliance with task completion through adaptive policy extraction and dual-objective optimization. Achieves 38% improvement in policy compliance while maintaining 20% higher task completion.

原文の言語: 英語

hcapo-hindsight-credit-assignment

Compute step-level credit assignments via hindsight generative verification: condition the LLM on successful outcomes to compute importance ratios that filter credit by causal relevance. Synergizes macro stability with micro precision.

原文の言語: 英語

hermes-hierarchical-video-memory

Use KV cache as hierarchical memory for real-time video stream understanding with minimal GPU overhead, achieving 10x faster response times compared to standard methods. Use when processing continuous video streams where latency and memory efficiency are…

原文の言語: 英語

heteroscale-autoscaling

Scale disaggregated LLM inference (prefill-decode) via topology-aware scheduling and metric-driven policies, achieving 26.6% GPU utilization improvement and conserving hundreds of thousands GPU-hours daily

原文の言語: 英語

hierarchical-lvm-reasoning

Reason about long-horizon dynamics by disentangling structure and motion in video VAE latents. Learn continuous latent motion chains that preserve temporal coherence while predicting terminal keyframes, enabling efficient reasoning about multi-step scenarios.

原文の言語: 英語

high-entropy-minority-tokens-rl

Optimize only high-entropy tokens during RL training to achieve better reasoning performance with 80% fewer gradient updates.

原文の言語: 英語

higher-order-linear-attention-mechanism

Enable data-dependent higher-order interactions in attention using prefix-sufficient statistics that maintain linear time and constant state, replacing quadratic dot-product attention while preserving expressivity through compact matrix operations.

原文の言語: 英語

himap-travel

Solves long-horizon planning problems with global constraints by decoupling planning into strategic (resource allocation) and tactical (execution) levels. Prevents constraint drift through synchronized state tracking and cooperative bargaining.

原文の言語: 英語

hop-skip-overthink-diagnosis

Novel error categorization framework examining failures across hops (diversity), coverage, and overthinking. Combines human annotation with automated metrics to diagnose why reasoning models hallucinate on multi-step tasks.

原文の言語: 英語

hpsv3-human-preference-score-evaluation

A VLM-based preference scoring system trained on 1.17M annotated comparisons to evaluate text-to-image generation quality at scale. Uses uncertainty-aware ranking loss for fine-grained assessment across diverse images and supports iterative quality…

原文の言語: 英語

humanomni-multimodal-reasoning

Improve multimodal reasoning by requiring explicit context understanding before reasoning. Use specialized reward mechanisms and context-aware training to prevent information-skipping shortcuts.

原文の言語: 英語

hybrid-linear-attention

Design hybrid architectures mixing linear and full attention at optimal ratios. Superior standalone linear models don't necessarily excel in hybrids; recall improves significantly with more full attention layers at ratios below 3:1, enabling efficient…

原文の言語: 英語

hybrid-reinforcement-learning

Combine sparse verifier rewards with dense reward model scores using stratified normalization to overcome limitations of either approach alone.

原文の言語: 英語

hyperagents-self-improvement

Enable AI systems to recursively improve themselves by making the meta-level modification procedure itself editable, achieving open-ended capability growth.

原文の言語: 英語

hypergraph-memory-rag

Build hypergraph-structured memory systems for multi-step RAG that capture high-order relationships between facts, enabling stronger reasoning across long contexts. Use when combining multiple retrieved documents in complex reasoning chains that require…

原文の言語: 英語

i-grpo-self-feedback-reasoning

Improve LLM reasoning through iterative refinement where the model refines its best previous attempts. Two-stage training: exploratory draft generation, then conditioned refinement using GRPO. Dynamic conditioning signals evolve with policy, enabling…

原文の言語: 英語

ieap-image-editing

Enable robust image editing by decomposing free-form instructions into sequential atomic operations executed through a neural program interpreter.

原文の言語: 英語