「reinforcement」の検索結果
13 件Scaling computer-use agents from individual actions to complex software work requires experience that connects
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe:
An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view.
To succeed in real-world long-horizon software development, coding agents must satisfy multi-dimensional task
On-policy reinforcement learning for large language models assumes that training responses are sampled from th
An effective agent memory system should adapt to a user's history and task requirements. Human-annotated super
Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous
Existing multi-agent collaboration (MAC) methods have primarily explored a group thinking collaboration topolo
Multi-agent systems (MAS) require both effective coordination and specialized task execution. Existing prompt
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language mod