Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning
Jian Hu, Huiying Li, Hao Zhang, Binfeng Xu et al. · arXiv · 2026
Abstract
A training framework for agentic reinforcement learning whose stated design goal is that a researcher — or a coding assistant — can hold the entire codebase in their head. The agent is an ordinary program, and a single asynchronous loop trains multimodal and mixture-of-experts policies while never learning from a token it did not itself generate. The authors report parity with a far heavier Megatron-based stack under a matched protocol.
Why it matters
Worth reading as an argument about research velocity rather than as a tool announcement: when every algorithm change has to thread through layers of framework glue, the framework has become the bottleneck.
https://arxiv.org/abs/2607.21653