Training & Fine-tuning

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Showing 91–118 of 118
  • NVIDIA Developer Blog · EN Agents & Tool Use
    Fine-Tuning NVIDIA Nemotron to Know Local Rules, Not Just Local Languages
    Fine-tuning NVIDIA Nemotron to know local rules, not just languages
    AI Agents Fine-tuning Generative AI Machine Learning NVIDIA
    As AI use spreads across languages and cultures, general-purpose models serve many needs. This NVIDIA post covers fine-tuning Nemotron models so they understand local rules as well as local languages, going beyond language support alone.
    Read original (NVIDIA Developer Blog) ↗
  • arXiv cs.LG (Machine Learning) · EN Training & Fine-tuning
    Displacement Moments and Path-Terminal Separation in Brownian Interpolant Sampling
    Neural Network Reinforcement Learning Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    LinMix: Data Mixing as Linear Domain Attribution
    Neural Network
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Same Verdict, Different Readers: Personalizing Fact-Checking Explanations from Relative Preferences
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Infrastructure & Hardware
    Agent-Conditioned Routing: Diverse Reasoning Paths Within One Frozen Model
    Fine-tuning GPT Mixture of Experts (MoE)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Uncovering Why LLMs Fail: From Internal Mechanisms to Adversarial Reasoning Trajectories
    Inference Llama Machine Learning Reinforcement Learning from Human Feedback (RLHF) Transformer
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Industry Adoption
    TRIDENT-Seq: Profile-Matched Prefix-Future Contrast for Label-Efficient Payment Risk Modeling
    Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • Sakana AI Blog (ja) · JA Industry Adoption
    Sakana API Platform、エンタープライズ向けにアーリーアクセスプログラムを開始
    Sakana AI opens early access to its enterprise Sakana API Platform
    Neural Network OpenAI
    Sakana AI announced Sakana API Platform for enterprises and opened an early access program. It offers the company's LLMs via API with admin controls to manage members and usage at the organization and workspace level. The API is OpenAI-compatible, so existing apps only need to switch endpoints. Sakana Fugu and Fugu Ultra come first, with Namazu and Fugu Max to follow.
    Read original (Sakana AI Blog (ja)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Aligning Large Language Models for Safety in African Languages: A Case Study of Hausa
    Fine-tuning Machine Learning Neural Network Reinforcement Learning Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Text Generation in Low-Resource Languages Using Human-Curated Vocabularies
    Retrieval-Augmented Generation (RAG) Reinforcement Learning Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Hierarchical Low-Rank Reparameterization for Parameter-Efficient
    Fine-tuning Llama Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    EnergyCal: Taming Loss Spikes in Muon-Family Optimizers for Sparse MoE Training
    Mixture of Experts (MoE) Retrieval-Augmented Generation (RAG) Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Exploring the Limits of the Delta Learning Hypothesis: Single-Pair Generator Biases in Direct Preference Optimization
    Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Guided-RLVR: Guided Exploration in Reinforcement Learning with Verifiable Rewards
    Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Multimodal
    V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models
    Computer Vision Embeddings Mixture of Experts (MoE) Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Developing a Speech Language Model for Basque
    Fine-tuning Llama Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Auto-Research Arena: Benchmarking and Advancing Auto-Research in Language Modeling
    Inference Neural Network Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR
    Fine-tuning Neural Network Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    Pretraining Knows What, Simulation Teaches How: Compositional Generalization in a Sim-Trained Robot Agent
    Computer Vision Fine-tuning GPT Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    SEMA-Auto: Sighted and Efficient Multi-Turn Attack with Autonomy
    DeepSeek Fine-tuning Neural Network Retrieval-Augmented Generation (RAG) Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains
    Fine-tuning Neural Network Reinforcement Learning Robotics
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • Publickey · JA New Model Releases
    AWSとGoogle Cloudが「PyTorch」のAWS Trainium、Google Cloud TPU対応版をオープンソースで公開。NVIDIAからの移行に挑む
    AWS and Google Cloud open-source PyTorch for Trainium and TPU
    Google Machine Learning NVIDIA
    At PyTorch Conference 2026 in California on October 20-21, AWS and Google Cloud each announced open-source PyTorch versions supporting their own AI processors, AWS Trainium and Google Cloud TPU, in a move aimed at drawing users away from NVIDIA.
    Read original (Publickey) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Industry Adoption
    Adapt for Planning, Not just Prediction: Optimistic Test-Time World Model Adaptation
    Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    VaLoP: Value Gradient Lookahead and Pullback for Reward-Guided Diffusion
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    Evolve on Graph: Self-Evolving Knowledge Graph Reasoning without Question Annotations
    AI Agents Neural Network Reinforcement Learning Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    Multi-Fidelity Training Reshapes Uncertainty Decomposition in Atomistic Models
    Fine-tuning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Beyond Modeling Order: Dual-Path Spatiotemporal Representation Learning for Cross-Subject EEG Emotion Recognition
    Deep Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    GPDPO: Learning from Reward Conflicts via Group Pareto-Dominance Policy Optimization
    Reinforcement Learning Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.CL (Computation and Language)) ↗