mlsanityData to 7 Oct 2026

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

arXiv 2607.21653 ↗First posted 22 Jul 2026cs.LG cs.CL cs.DCcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Agentic reinforcement learning requires rapid experimentation with agents and learning algorithms, yet large policies and long, multimodal trajectories demand substantial distributed infrastructure. We present MOLT, a lightweight, PyTorch- and Hugging Face-native framework that brings these goals together through four contributions. MOLT combines direct loading of Hugging Face models with experimentally validated trillion-parameter scalability in approximately 9.2K lines of framework code. Unified OpenAI- and Anthropic-compatible interfaces integrate existing agents with automatic handling of context compaction. Fully asynchronous training overlaps agent rollouts and policy optimization, accommodating variable agent execution times. Distributed experience storage removes centralized rollout-memory bottlenecks for long, multimodal trajectories. We experimentally validate the complete RL training pipeline on a one-trillion-parameter policy and demonstrate sustained learning with a 30B mixture-of-experts agent, establishing MOLT as a lightweight foundation for large-scale agentic RL research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 23 of its 32 references that are in the map. First posted on arXiv 22 Jul 2026.

Builds on

23 of 32 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents19 Mar
  2. 2025RollArt: Disaggregated Multi-Task Agentic RL Training at Scale27 Dec
  3. 2025Agent Lightning: Train ANY AI Agents with Reinforcement Learning5 Aug

Other references · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  4. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,678
  5. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 1,980
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  7. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,276
  8. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  9. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,107
  10. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 641
  11. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  12. 2025AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoningcited by 234
8 more
  1. 2025REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalizationcited by 233
  2. 2025Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routerscited by 67
  3. 2025StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generationcited by 56
  4. 2025Laminar: A Scalable Asynchronous RL Post-Training Frameworkcited by 35
  5. 2026DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Trainingcited by 13
  6. 2026Polar: Agentic RL on Any Harness at Scalecited by 12
  7. 2026Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scalecited by 7
  8. 2026RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learningcited by 3

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  3. 2025AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework
  4. 2025Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
  5. 2025EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models
  6. 2025VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use
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