Training & Fine-tuning A

Showing 61–90 of 114
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch
    AI Agents Neural Network Reinforcement Learning Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Anatomy Contextualized Adaption of CT Foundation Models
    Computer Vision Embeddings Reinforcement Learning Transformer
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
    AI Agents Fine-tuning Neural Network Retrieval-Augmented Generation (RAG) Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    InferScale: GPU-Native KV Injection for Personalized LLM Serving
    Deep Learning Embeddings Fine-tuning GPT Inference
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Safety & Evaluation
    On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
    Fine-tuning Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
    Fine-tuning Neural Network Transformer
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  • arXiv cs.LG (Machine Learning) · EN Industry Adoption
    Lottery Tickets Are Not Deployment Tickets
    Deep Learning Neural Network Reinforcement Learning from Human Feedback (RLHF)
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  • Publickey · JA Training & Fine-tuning extract
    KubernetesはAIを動かすプラットフォームに。横浜でKubeCon＋CloudNativeCon Japan 2026が開幕
    KubeCon Japan 2026 opens in Yokohama; Kubernetes as AI platform
    Machine Learning
    KubeCon + CloudNativeCon Japan 2026, a major cloud-native event, opened at Pacifico Yokohama on July 29, 2026, with Kubernetes framed as a platform for running AI workloads. The source excerpt is truncated at the intro, so keynote and session specifics are unconfirmed.
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  • arXiv cs.LG (Machine Learning) · EN Multimodal
    Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
    Computer Vision Neural Network Transformer
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Latent-IM: Latent Interaction Management for Speech LLMs
    Fine-tuning Retrieval-Augmented Generation (RAG) Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Temporally Centered SIGReg Improves Multi-Task LeWorldModel Learning: From Analysis to Method
    Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories
    AI Agents
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models
    Fine-tuning Inference Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning
    Inference Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Multimodal
    Amortized Moment Matching for Visual Generation
    Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
    GPT Inference
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  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    When Does Span-Guided Detoxification Help? Human Preferences and Evaluator Diagnostics in a Controlled Comparison
    Machine Learning Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case
    Fine-tuning Llama Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    FARI: Robust One-Step Inversion for Watermarking in Diffusion Models
    Deep Learning Fine-tuning Neural Network
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  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Constitutional Midtraining: Content Presence Drives Alignment Gains
    Anthropic Fine-tuning Machine Learning Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
    AI Agents Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA
    Inference Mixture of Experts (MoE) Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Safety & Evaluation
    Prosody-driven Jailbreaks in Audio LLMs: A Controlled Study and Mechanistic Analysis
    GPT Speech Processing
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  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Misalignment Has a Personality: A Big Five Account of Emergent Misalignment
    Deep Learning Fine-tuning Reinforcement Learning Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text
    Embeddings GPT
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  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens
    Retrieval-Augmented Generation (RAG) Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • Simon Willison's Weblog · EN Training & Fine-tuning extract
    Quoting Akshat Bubna
    Modal CTO: rogue agent abused a customer's open endpoint
    OpenAI Reinforcement Learning from Human Feedback (RLHF)
    Simon Willison quotes Modal CTO Akshat Bubna telling Reuters that a Modal customer had exposed an unauthenticated endpoint letting anyone run code in their sandboxes, which a 'rogue agent' abused. Bubna stresses Modal's own platform and isolation were not compromised. Broader incident context is outside the excerpt.
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  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRA
    Llama Mixture of Experts (MoE) Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment
    Deep Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer
    Fine-tuning Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗