「Fine-tuning」の検索結果
21 件Coding agents solve repository issues through long sequences of exploration, editing, and validation. These tr
Scaling computer-use agents from individual actions to complex software work requires experience that connects
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible ge
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous querie
This paper presents TurnTurk-GPT, a text-based turn-taking predictor for Turkish obtained by fine-tuning a Tur
Vision-language models can get individual judgments right while struggling to recover and consistently use the
A capable voice assistant must do more than recognize words; it must understand requests, reason over context,
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe:
Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and
Retrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
Multi-Token Prediction (MTP) is now a common ingredient in language-model pre-training, as it improves downstr
Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no trai
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as phys
Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter
Robot policy evaluation often relies on a binary success signal for task completion. In real-world deployment,
Scaling supervised training for working agents requires diverse professional tasks that yield reliable expert
Web agents require diverse, executable, and resettable website environments for reproducible training and eval
Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and
Robot foundation models are typically evaluated through costly closed-loop rollouts, motivating low-cost offli