跑智能体RL想少折腾工程？NVIDIA开源Molt，8.6K行代码整合Ray与vLLM，效果比肩Megatron方案。
英伟达开源Molt，一个基于PyTorch的智能体强化学习框架，核心RL代码约8.6K行。Molt以单个异步循环整合Ray、vLLM和NeMo AutoModel，让智能体保持普通Python实现，轨迹保持token级精确。吞吐量与基于Megatron的堆栈统计上相当。该框架旨在降低主流框架中算法修改需串联训练器、分布式后端和rollout胶水代码的工程成本。
NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework
Agentic RL research is constant algorithm modification, and in mainstream frameworks every change threads through trainer, distributed backend, and rollout glue. NVIDIA's Molt targets that cost with about 8.6K lines of RL code, composing Ray, vLLM, and NeMo AutoModel around one asynchronous loop. The agent stays ordinary Python, trajectories stay token-exact, and throughput comes out statistically comparable to a Megatron-based stack. The post NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework appeared first on MarkTechPost .