NVIDIA has reset the "training–deployment" interface of Agentic RL to the native PyTorch layer, and today released the Molt framework together with Hugging Face. arXiv 2607.21653, submitted July 22; the GitHub repo NVIDIA-NeMo/labs-molt is now public. Traditional Agent RL frameworks stack trainer / distributed backend / rollout in three layers, so every algorithmic change has to ripple through the entire pipeline. Molt inverts this: the whole agent is just an ordinary program, and a single async loop simultaneously drives multimodal and MoE policies. Token, policy version, and model semantics remain strictly consistent between rollout and training — guaranteeing the model never updates weights against tokens it didn't generate. Implementation-wise, Molt isn't tied to Megatron or vLLM — instead it's "small enough and clean enough" for an AI coding assistant to read the entire repo in one go. Under a matched async protocol, Molt is statistically on par with a Megatron-based SOTA stack, paying virtually no performance cost for its leanness. An open-source license, ready-to-use recipes, and containers are all provided — so Agent RL researchers no longer have to wrestle with distributed glue code. Two points worth flagging. First, this is the first time NVIDIA has made "an agent is just an ordinary program" a first-class citizen in a framework, meaning tool calls, external environment interaction, and long-horizon decisions inside the agent loop can flow seamlessly into RL training, rather than being wrapped by an external adapter. Second, the combination of async rollout + MoE + multimodal pushes NVIDIA's training stack further into the "fully async, native PyTorch" unified direction after NeMo AutoModel, freeing large-model RLHF/Agent training from its dependence on specialized RL frameworks. Bottom line: Molt hits the long-standing pain point of "Agent RL is hard to reproduce and expensive to iterate on". When a framework becomes small enough to be understood by an LLM in one read, Agent RL stops being an engineering moat for a few big companies and becomes a reproducible experiment for ordinary labs — a key piece of infrastructure as Agent research scales in the second half of 2026.