New research from NVIDIA. They just dropped a PyTorch-native training framework for agentic RL. (bookmark it) Paper summary: Molt is a PyTor…

New research from NVIDIA. They just dropped a PyTorch-native training framework for agentic RL. (bookmark it) Paper summary: Molt is a PyTorch-native agentic RL framework with an unusual design target. The codebase should be compact enough for a researcher to hold in their head, and for an AI coding assistant to read and reason about in its entirety. Agentic RL research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes. In mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue, and that cost falls on the researcher every iteration. The agent stays an ordinary program. One asynchronous loop trains multimodal and mixture-of-experts policies while never training on a token it did not generate, staying consistent in tokens, policy versions, and model semantics. Leanness does not cost throughput. Under a matched, fully asynchronous protocol Molt comes out statistically comparable to a state-of-the-art Megatron-based stack. Readable by an AI coding assistant is now a stated design constraint on research infrastructure. Recipes and containers are open source at http://github.com/NVIDIA-NeMo/labs-molt Paper: https://arxiv.org/abs/2607.21653 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-07-27