Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning
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.
View Cached Full Text
Cached at: 07/27/26, 05:40 AM
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.
View arXiv pageView PDFGitHub605Add to collection
Get this paper in your agent:
hf papers read 2607\.21653
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2607.21653 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2607.21653 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2607.21653 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
@dair_ai: New research from NVIDIA. They just dropped a PyTorch-native training framework for agentic RL. (bookmark it) Paper sum…
NVIDIA released Molt, a PyTorch-native agentic RL framework designed for compactness and readability, with performance comparable to Megatron-based stacks. The framework is open-source and includes a paper.
Miles: A PyTorch-Native Stack for Large-Scale LLM RL Post-Training (14 minute read)
Miles is an open-source PyTorch-native framework from RadixArk for large-scale LLM reinforcement learning post-training, integrating SGLang, Megatron-LM, and Ray for high-throughput rollout and distributed training.
@PyTorch: Built on PyTorch, Ray, SGLang, and NVIDIA Megatron-LM, Miles is an open source framework from RadixArk for large-scale …
Miles is an open source framework from RadixArk for large-scale LLM reinforcement learning post-training, integrating PyTorch, Ray, SGLang, and NVIDIA Megatron-LM with support for MoE, low-precision, and fault tolerance.
@_akhaliq: LiteResearcher A Scalable Agentic RL Training Framework for Deep Research Agent
LiteResearcher is a scalable reinforcement learning training framework designed for deep research agents.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
EvoTrainer introduces an autonomous training framework that co-evolves LLM policies and training harnesses through empirical feedback, outperforming human-engineered RL baselines on mathematical reasoning, code generation, and long-horizon software engineering tasks.