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#Policy Gradient

ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

Xiaoxuan Wang, Han Zhang et al.Feb 25arXiv

This paper tackles why training AI agents that act over many steps (like browsing the web or moving in a house) often becomes unstable and collapses.

#Agentic Reinforcement Learning#Policy Gradient#Sequence-level Clipping

Multi-Task GRPO: Reliable LLM Reasoning Across Tasks

Shyam Sundhar Ramesh, Xiaotong Ji et al.Feb 5arXiv

Large language models are usually trained to get good at one kind of reasoning, but real life needs them to be good at many things at once.

#Multi-Task Learning#GRPO#Reinforcement Learning Post-Training

AT$^2$PO: Agentic Turn-based Policy Optimization via Tree Search

Zefang Zong, Dingwei Chen et al.Jan 8arXiv

AT2PO is a new way to train AI agents that work in several turns, like asking the web a question, reading the result, and trying again.

#Agentic Reinforcement Learning#Turn-level Optimization#Tree Search

Meta-RL Induces Exploration in Language Agents

Yulun Jiang, Liangze Jiang et al.Dec 18arXiv

This paper introduces LAMER, a Meta-RL training framework that teaches language agents to explore first and then use what they learned to solve tasks faster.

#Meta-Reinforcement Learning#Language Agents#Exploration vs Exploitation