MDP-GRPO: Stabilized Group Relative Policy Optimization for Multi-Constraint Instruction Following
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Computer Science > Machine Learning
Title:MDP-GRPO: Stabilized Group Relative Policy Optimization for Multi-Constraint Instruction Following
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. We identify and formalize three pathologies of z-score group normalization in this regime: low-variance amplification, mean-centering blindness, and zero-variance collapse. To address them, we propose MDP-GRPO, which stabilizes learning through (1) multi-temperature sampling to increase reward dispersion, (2) dual-anchor advantages to restore gradients in homogeneous groups and stop mean-centering blindness, (3) prospect-theoretic shaping to bound updates and penalize violations based on Kahneman and Tversky's theory, and (4) asymmetric KL regularization. Evaluated on FollowBench, IFEval, and a curated multi-constraint dataset, MDP-GRPO outperforms standard GRPO, improving strict constraint satisfaction by up to 5.0% on Llama-3.2-3B. Our method also enables stable convergence with small group sizes while preserving general capabilities on MMLU and ARC.
| Comments: | Accepted to ACL 2026 Main Conference. 14 pages, 9 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.06058 [cs.LG] |
| (or arXiv:2606.06058v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06058
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Mohammad Mahdi Salmani-Zarchi [view email][v1] Thu, 4 Jun 2026 11:58:59 UTC (579 KB)
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