MOLT is a lightweight framework that combines direct Hugging Face model loading with distributed training for . It provides unified interfaces for OpenAI and Anthropic agents, automatically handles , and stores complex trajectory experience in a distributed object store to remove centralized rollout-memory bottlenecks. The full training pipeline was validated on a one-trillion-parameter policy, and sustained learning was demonstrated with a 30B mixture-of-experts agent. With roughly 9.2K lines of framework code, the authors argue this makes large-scale agentic RL experimentation far more accessible.
A lightweight PyTorch-native framework matches Megatron-based agentic RL training performance.
Molt simplifies agentic RL research with a compact, auditable codebase while maintaining scalability for multimodal and mixture-of-experts policies.
Big Tech
Jian Hu · Huiying Li · Hao Zhang · Binfeng Xu · Yifan Zhang · Shaokun Zhang · +5 more
NVIDIA
Research Digest··2 min read
Thread:Agent Harness Optimization
In one line
Molt provides scalable PyTorch-native agentic RL training in 9.2K lines, with trillion-parameter support, asynchronous execution, and distributed experience storage.
Before you read this
Three ideas this paper builds onagentic reinforcement learningTraining approach that lets LLM-based agents learn multi-step tool use and planning via reinforcement learning, often with dense or per-tool rewards.102 papers →context compactionCompressing or summarizing an LLM's past context into a smaller representation to extend memory and improve reasoning within limited token budgets.71 papers →mixture of expertsA neural architecture with many specialized sub-networks, only a few activated per input via a router, balancing capacity and efficiency.66 papers →
Read it as
Hu et al. introduce Molt, a PyTorch-native training framework for agentic reinforcement learning that prioritizes researcher readability and end-to-end algorithm traceability.
The authors designed Molt as a compact, three-component system consisting of an asynchronous loop that trains multimodal and mixture-of-experts (MoE) policies.
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