Title: Listwise Generalized Preference Optimization with Process-aware Signals for LLM Reasoning
Primary area: reinforcement learning
Keywords: RL Optimization, listwise ranking

Abstract: Standard preference optimization methods for LLMs suffer from two limitations: pairwise objectives like DPO discard valuable ranking information, and outcome-only supervision provides sparse feedback for multi-step reasoning. We propose Listwise Generalized Preference Optimization with Process-Aware signals (LGPO-PA), which combines listwise ranking objectives with dense process-level supervision. Our method scores multiple candidate responses using step-level process rewards, execution feedback, and consistency checks, then optimizes a convex listwise loss. Across mathematical reasoning (GSM8K, MATH), code generation (HumanEval, MBPP), and multi-hop QA (HotpotQA), LGPO-PA outperforms pairwise methods by 8-12\% and listwise methods without process signals by 6-9\%, while maintaining full offline operation. Ablations confirm that listwise optimization (+4.2\%) and process-aware scoring (+5.1\%) provide complementary benefits.