Molt
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Agentic RL research is constant algorithm modification, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue. NVIDIA's Molt is a PyTorch-native training framework built to make that cost small.
Readable by design: The stated target is a codebase compact and clean enough for a researcher to hold in their head, and for an AI coding assistant to read and reason about in its entirety, so the algorithm flow can be traced and changed end to end.
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.
Throughput holds: Under a matched, fully asynchronous protocol, Molt comes out statistically comparable to a state-of-the-art Megatron-based stack, so the simplicity does not show up as a throughput penalty.
Why it matters: Recipes and containers are open source, and the framing is notable on its own. Being legible to an AI coding assistant now sits alongside throughput as a stated design constraint on research infrastructure.
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