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ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

Beginner

Qiang Zhang, Boli Chen et al.Jan 10arXiv

ArenaRL teaches AI agents by comparing their answers against each other, like a sports tournament, instead of giving each answer a single noisy score.

#ArenaRL#reinforcement learning#relative ranking

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

Intermediate

Shih-Yang Liu, Xin Dong et al.Jan 8arXiv

When a model learns from many rewards at once, a popular method called GRPO can accidentally squash different reward mixes into the same learning signal, which confuses training.

#GDPO#GRPO#multi-reward reinforcement learning

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

Intermediate

Junbo Li, Peng Zhou et al.Dec 18arXiv

Turn-PPO is a new way to train chatty AI agents that act over many steps, by judging each conversation turn as one whole action instead of judging every single token.

#Turn-PPO#multi-turn reinforcement learning#agentic LLMs

Papers3

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs