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VRPRM: Process Reward Modeling via Visual Reasoning
通过视觉推理提升过程奖励建模精度，为复杂任务训练提供新思路。
arXiv:2508.03556v3 Announce Type: replace Abstract: Process Reward Model (PRM) is widely used in the post-training of Large Language Model (LLM) becau…
2
Leveraging Verifier-Based Reinforcement Learning in Image Editing
将RLHF引入图像编辑的新范式，提出基于验证器的强化学习解决奖励模型缺失瓶颈。
arXiv:2604.27505v2 Announce Type: replace Abstract: While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-…
3
rePIRL: Learn PRM with Inverse RL for LLM Reasoning
用逆强化学习从推理轨迹中自动学习过程奖励模型，有效提升大语言模型的复杂推理能力。
arXiv:2602.07832v2 Announce Type: replace Abstract: Process rewards have been widely used in deep reinforcement learning to improve training efficienc…
4
Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
ICLR 2026 顶会论文：用信息论指导消除奖励模型中的归纳偏置，为强化学习对齐提供更客观的评估基础
arXiv:2512.23461v2 Announce Type: replace Abstract: Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align la…
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CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
用奖励模型突破测试用例限制，实现代码大模型训练与推理阶段的可扩展强化学习。
arXiv:2602.17684v2 Announce Type: replace Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large lan…