LLM智能体强化学习综述：500+文献映射能力与应用

Nice survey paper mapping agentic reinforcement learning for LLMs, showing how models learn by acting across time.

Covers 500+ works and groups them into a 2-part map of capabilities and applications.

The problem is that common LLM training rewards a single answer once, then stops learning.

Real tasks need many steps, partial information, and choices that affect what happens later.

The survey formalizes that setup as an agent that sees a bit, chooses an action, and gets feedback.

That perspective uses memory to track context, planning to pick sequences, and tools to affect the world.

It also includes reasoning for constraint handling, perception for multimodal inputs, and self-improvement to refine policies.

Reinforcement learning links all of this, because rewards arrive after sequences, so the policy learns what to try next.

Paper – arxiv. org/abs/2509.02547

Paper Title: "The Landscape of Agentic Reinforcement Learning for LLMs: A Survey"