Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning
Molt’s pitch is that agentic RL experiments should be editable end to end inside a compact PyTorch codebase.
The framework treats the agent as an ordinary program and uses one asynchronous loop for training. Its authors say it supports multimodal and mixture-of-experts policies while keeping token generation, policy versions, and model semantics consistent. They report performance statistically comparable to a state-of-the-art Megatron-based stack under a matched fully asynchronous protocol. Molt is open source, with recipes and containers provided. HF Daily Papers' note
The framework treats the agent as an ordinary program and uses one asynchronous loop for training. Its authors say it supports multimodal and mixture-of-experts policies while keeping token generation, policy versions, and model semantics consistent. They report performance statistically comparable to a state-of-the-art Megatron-based stack under a matched fully asynchronous protocol. Molt is open source, with recipes and containers provided. HF Daily Papers' note
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