💡 The paper introduces Molt, a scalable PyTorch-native training framework for agentic reinforcement learning. The problem addressed is the high cost of modifying algorithms, estimators, pipelines, and rollout schemes in existing frameworks, which can lead to significant overhead for researchers. Molt aims to keep this cost small by providing a compact and clean codebase that is easy for researchers to understand and modify.

The method involves designing Molt as a PyTorch-native framework, where the agent is an ordinary program that can train multi-modal and mixture-of-experts policies in a single asynchronous loop. This approach allows for consistent tokenization, policy versions, and model semantics, without requiring the agent to train on tokens it did not generate.

The results show that Molt is statistically comparable to a state-of-the-art Megatron-based stack, even when using an unmatched, fully asynchronous protocol. This demonstrates that the lean design of Molt does not come at the cost of performance. Additionally, Molt is open-source and provides recipes and containers, making it accessible to researchers and practitioners.

Overall, the paper contributes a novel framework for agentic reinforcement learning that prioritizes simplicity, flexibility, and performance. By providing a clean and compact codebase, Molt enables researchers to easily modify and extend the framework, reducing the overhead associated with algorithmic changes and promoting faster innovation in the field.