Agentic reinforcement learning – Definition & Meaning

Definition of agentic reinforcement learning

: the use of reinforcement learning to train an AI agent through multi-step interaction with tools or an environment, so that it learns strategies for planning, acting, recovering from errors, and completing long-horizon tasks
- The coding system used agentic reinforcement learning on complete repository tasks rather than rewarding isolated code completions.
- Agentic RL must assign credit across a trajectory in which an early search query may determine whether the final answer succeeds.

Origin & history

first known use2025

Agentic reinforcement learning became an established research label in 2025 as researchers distinguished reinforcement learning for interactive, multi-step agents from reinforcement learning applied to a single language-model response. A 2025 survey formalized Agentic RL around temporally extended, partially observable environments, and the term became common in work on coding, search, tool use, and other long-horizon agents.

Editorial references

Sources & further reading

3 sources

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey Research Survey · Transactions on Machine Learning Research / arXiv · 2025-09-02
SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering Research Paper · arXiv · 2026-10-14
ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation Research Paper · arXiv · 2026-10-14

Test yourself

Which of these is the meaning of agentic reinforcement learning?

Cite this entry

"Agentic reinforcement learning." AI Dictionary, Dadgogo, https://dadgogo.com/dictionary/agentic-reinforcement-learning/. Accessed 15 Oct. 2026.