EPISODE · Dec 25, 2025 · 12 MIN
Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
from Build Wiz AI Show · host Build Wiz AI
In this episode, we explore Agent-R1, a modular framework designed to transform Large Language Models from static text generators into autonomous agents capable of active environmental interaction. We dive into how extending the Markov Decision Process (MDP) framework enables these agents to master multi-turn dialogues, utilize external tools, and benefit from dense process rewards. Finally, we discuss how end-to-end reinforcement learning is setting new performance benchmarks in complex tasks like multi-hop reasoning by refining how models learn from their own actions.
What this episode covers
In this episode, we explore Agent-R1, a modular framework designed to transform Large Language Models from static text generators into autonomous agents capable of active environmental interaction. We dive into how extending the Markov Decision Process (MDP) framework enables these agents to master multi-turn dialogues, utilize external tools, and benefit from dense process rewards. Finally, we discuss how end-to-end reinforcement learning is setting new performance benchmarks in complex tasks like multi-hop reasoning by refining how models learn from their own actions.
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Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
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