arXiv Machine Learning
Jun 5

MDP-GRPO: Stabilized Group Relative Policy Optimization for Multi-Constraint Instruction Following

arXiv:2606. 06058v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards is ideal for multi-constraint instruction following, yet standard group-relative policy optimization (GRPO) becomes unstable under discrete, low-dispersion rewards, where within-group reward distributions are frequently homogeneous.

By Mohammad Mahdi Salmani-Zarchi, Zahra Rahimi, Heshaam Faili, Mohammad Javad Dousti
arXiv Machine Learning
Oct 3

Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients

The paper investigates why Group Relative Policy Optimization (GRPO) benefits from per‑prompt normalization by examining the local curvature of the sequence‑level policy gradient. It shows that standard deviation normalization acts as an adaptive gradient, yielding a provably faster convergence rate than unnormalized REINFORCE under mild conditions, with the improvement tied to the average within‑prompt reward standard deviation. The authors also propose IS‑GRPO, an importance‑sampling variant that maintains alignment with the full gradient and offers a tighter convergence guarantee, and empirically validate these theoretical insights on GSM8K and MATH datasets at 1.5B and 7B model scales.

By Cheng Ge, Caitlyn Heqi Yin, Hao Liang, Jiawei Zhang
arXiv AI
Jun 2

Coordination Graphs for Constrained Multi-Agent Reinforcement Learning

arXiv:2606. 02337v1 Announce Type: new Abstract: Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requirements couple agents in ways that reward structure alone does not capture.

By Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson