The Peril of Preference: Why GRPO fails on Ordinal Rewards

• URL: http://arxiv.org/abs/2511.04439v1
• Date: Thu, 06 Nov 2025 15:12:50 GMT
• Title: The Peril of Preference: Why GRPO fails on Ordinal Rewards
• Authors: Anisha Garg, Ganesh Venkatesh,
• Abstract summary: We introduce Correctness Relative Policy Optimization (CoRPO), a new formulation that solves this flaw.<n>CoRPO uses an adaptive baseline that enforces a minimum quality threshold, ensuring failed solutions are never positively reinforced.<n>We empirically validate CoRPO on a code verification task, where it demonstrates more stable convergence and better out-of-domain generalization.
• Score: 0.8937905773981699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Group-relative Policy Optimization's (GRPO) simplicity makes it highly desirable for adapting LLMs to become experts at specific tasks. But this simplicity also makes it ill-specified as we seek to enhance RL training with richer, non-binary feedback. When using ordinal rewards to give partial credit, GRPO's simplicity starts to hurt, as its group-average baseline often assigns a positive advantage to failed trajectories and reinforces incorrect behavior. We introduce Correctness Relative Policy Optimization (CoRPO), a new formulation that solves this flaw. CoRPO uses an adaptive baseline that enforces a minimum quality threshold, ensuring failed solutions are never positively reinforced. Once the policy consistently meets this threshold, the baseline automatically transitions to a relative preference mode, pushing the model to find optimal solutions rather than just "acceptable" ones. We empirically validate CoRPO on a code verification task, where it demonstrates more stable convergence and better out-of-domain generalization. This work represents a critical step in our broader research program to enable LLMs to learn genuinely new capabilities through reinforcement learning. We achieve this by enabling LLMs to learn from rich, multi-dimensional feedback - progressing from binary to ordinal rewards in this work, and onward to denser, per-step supervision.

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