Hardware
How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo
Autonomous coding agents such as Codex (GPT‑5.5) can fully automate reinforcement‑learning research workflows by provisioning GPU‑hosted environments, orchestrating experiments, and iteratively optimi
Autonomous coding agents such as Codex (GPT‑5.5) can fully automate reinforcement‑learning research workflows by provisioning GPU‑hosted environments, orchestrating experiments, and iteratively optimizing models with NVIDIA NeMo RL and NeMo Gym. Leveraging specialized agent skills—system hygiene, state‑preserving memory, and autoresearch—these agents enable reproducible, long‑running ML pipelines that support hypothesis branching, baselining, and ledgered campaign tracking within a single repository. In practice, Codex has translated RL literature into running code (e.g., implementing the OAPL off‑policy algorithm) and achieved significant performance gains, raising accuracy on custom vision‑language tasks from 25 % to 96.9 %.
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Source: NVIDIA Developer | 2026-07-14