arXiv paper introduces branching policy optimization for sandbox-native language agents; critiques RLHF-based rollout topologies in reinforcement learning
Read the original at arxiv.org→arXiv:2607.14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes. State-of-the-art...
Original headline: "Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning"