A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes

Key points

• Reinforcement learning (RL) has emerged as a critical phase in the post-training pipeline of large language models (LLMs).1
• As the frontier of LLM capabilities shifts from static reasoning to agentic tasks--characterized by external tool invocation, multi-turn interactions, and long-horizon decision-making--existing mainstream RL frameworks have grown increasingly unwieldy, raising the overhead of adoption, usage, and debugging.1
• Built upon Megatron for distributed training and SGLang for high-throughput inference, LoongSage centralizes orchestration within a single controller process and encapsulates all GPU-resident components as Ray actors.1
• Beyond agentic RL, LoongSage incorporates a memory-efficient Teacher Manager that decouples the scaling of teacher models from GPU memory constraints, natively supporting Multi-Teacher On-Policy Distillation.1

Sources (1)

• [1]A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 08:08 AM

  To address these challenges, we present LoongSage, an agent-native, fully asynchronous RL framework with a lightweight core of roughly 14K lines of code.

  Reinforcement learning (RL) has emerged as a critical phase in the post-training pipeline of large language models (LLMs).

Extractive summary: sentences quoted from the sources.