RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction

arXiv:2608.06310 · cs.LG, cs.CL · Submitted 2026-08-06 · Read on arXiv

Chenglong Wang, Ziming Zhu, Yifu Huo, Bei Li, Qiaozhi He, Yan Ding, Xiaoyang Hao, Yuxin Gao, Tianhua Zhou, Xiaojia Chang, Tongran Liu, Jingbo Zhu

cs.LG, cs.CL

Submitted: 2026-08-06

Code: https://github.com/wangclnlp/RRC

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• Aggregation — 13× in this paper · explained in 1 episode
• Accuracy — 11× in this paper · explained in 1 episode
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Ranking-based Reward Construction — 9× in this paper
• Anchor-Guided Ranking — 8× in this paper
• Self-Competitive Ranking — 8× in this paper
• GRPO — 7× in this paper · explained in 6 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 3 episodes
• Pairwise Comparisons — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 3× in this paper · explained in 25 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Verifiable Rewards — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Ablation Study on Conflict-Aware Ranking Adjustment — 1× in this paper
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Reasoning — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 5 episodes
• Query Complexity — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• WorldPM: Scaling Human Preference Modeling
• Unified Reward Model for Multimodal Understanding and Generation

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks