AMIR-GRPO: Inducing Implicit Preference Signals into GRPO

• URL: http://arxiv.org/abs/2601.03661v1
• Date: Wed, 07 Jan 2026 07:22:58 GMT
• Title: AMIR-GRPO: Inducing Implicit Preference Signals into GRPO
• Authors: Amir Hossein Yari, Fajri Koto,
• Abstract summary: Reinforcement learning has become the primary paradigm for aligning large language models on complex reasoning tasks.<n> GRPO is widely used in large-scale post-training but faces structural limitations in reasoning-heavy settings.<n>AMIR-GRPO augments GRPO with an implicit DPO-style contrastive regularizer constructed directly from intra-group reward rankings.
• Score: 15.759757442328388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning has become the primary paradigm for aligning large language models (LLMs) on complex reasoning tasks, with group relative policy optimization (GRPO) widely used in large-scale post-training. However, GRPO faces structural limitations in reasoning-heavy settings: sequence-level advantage normalization introduces systematic length bias, penalties for low-quality trajectories are diluted, and the scalar objective discards rich pairwise preference information embedded in within-group reward rankings. As a result, valuable supervision from costly rollouts remains underutilized. We propose AMIR-GRPO, which augments GRPO with an implicit DPO-style contrastive regularizer constructed directly from intra-group reward rankings, requiring no additional annotations. This mechanism amplifies suppression of low-reward trajectories, attenuates response-level length bias, and transforms each rollout group into a denser set of supervision constraints. Across multiple mathematical reasoning benchmarks, AMIR-GRPO consistently outperforms strong GRPO baselines, yields clearer separation between correct and incorrect reasoning chains, and delivers broader coverage gains beyond the subset of instances solved by standard GRPO.

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