Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning

Hugging Face Daily Papers Papers
pytorch reinforcement-learning agentic-rl training-framework open-source scalable nvidia

Summary

Molt is a PyTorch-native training framework for agentic reinforcement learning designed to be compact and clean for easy modification, while achieving performance comparable to Megatron-based stacks.

Agentic reinforcement learning research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue: the cost lands on the researcher at every iteration. Molt is a PyTorch-native training framework built to keep that cost small: 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 is an ordinary program, and one asynchronous loop trains multimodal and mixture-of-experts policies while never training on a token it did not generate, consistent in tokens, policy versions, and model semantics. Leanness does not cost performance: under a matched, fully asynchronous protocol, Molt is statistically comparable to a state-of-the-art Megatron-based stack. Molt is open source and provides recipes and containers at https://github.com/NVIDIA-NeMo/labs-molt.
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Paper page - Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning

Source: https://huggingface.co/papers/2607.21653

Abstract

Agenticreinforcementlearningresearchisconstantalgorithmmodification,newestimators,newpipelinestages,newrolloutschemes,andinmainstreamframeworkseachchangethreadsthroughlayersoftrainer,distributedbackend,androlloutglue:thecostlandsontheresearcherateveryiteration.MoltisaPyTorch-nativetrainingframeworkbuilttokeepthatcostsmall:acodebasecompactandcleanenoughforaresearchertoholdintheirhead,andforanAIcodingassistanttoreadandreasonaboutinitsentirety,sothealgorithmflowcanbetracedandchangedendtoend.Theagentisanordinaryprogram,andoneasynchronouslooptrainsmultimodalandmixture-of-expertspolicieswhilenevertrainingonatokenitdidnotgenerate,consistentintokens,policyversions,andmodelsemantics.Leannessdoesnotcostperformance:underamatched,fullyasynchronousprotocol,Moltisstatisticallycomparabletoastate-of-the-artMegatron-basedstack.Moltisopensourceandprovidesrecipesandcontainersathttps://github.com/NVIDIA-NeMo/labs-molt.

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