Training Recipes for Agentic Reinforcement Learning in LLMs: A Survey

Standard alignment pipelines like RLHF often fail to instill the robustness required for complex agentic tasks. This survey systematizes Agentic Reinforcement Learning, a paradigm that optimizes policies directly against environmental feedback within a Contextual POMDP framework. Unlike prior reviews cataloging agent capabilities, we focus on the engineering of training recipes necessary to build generalist agents from scratch. Our taxonomy examines schemes for rollout and optimization, infrastructure for high-throughput training, and environments that formalize interaction settings, alongside evaluation benchmarks distinguishing active training gyms from held-out certifications. By synthesizing these disparate components into a unified framework, we aim to accelerate the development of robust, nextgeneration autonomous agents.