The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling...

The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling massive multi-modal datasets. Scientific AI agents can take on much of that busywork, acting as assistants that review literature, generate hypotheses, plan experiments, submit computational jobs, orchestrate lab operations, analyze results…

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When training large language models (LLMs) with reinforcement learning from verifiable rewards (RLVR), one of the most compelling questions is how to overcome...

When training large language models (LLMs) with reinforcement learning from verifiable rewards (RLVR), one of the most compelling questions is how to overcome performance plateaus. The previous NVIDIA Research solution, Prolonged Reinforcement Learning (ProRL), showed that adding more reinforcement learning (RL) steps during prolonged training could expand the reasoning boundaries of LLMs.

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