1
The Bidirectional Process Reward Model
提出双向过程奖励模型，突破传统单向奖励局限，提升语言模型推理与对齐性能。
arXiv:2508.01682v3 Announce Type: replace Abstract: Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps wit…
2
StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
逐步过程奖励引导LLM微调，提升RTL合成质量的创新方法
arXiv:2606.04246v1 Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon …
3
ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
提出ARBOR框架，用可复用评分缓冲为搜索代理提供在线过程奖励，显著提升推理与搜索效率。
arXiv:2606.03239v1 Announce Type: new Abstract: LLM-based search agents are trained predominantly with outcome-only reward, leaving the search process…
4
Verifiable Process Rewards for Agentic Reasoning
提出可验证过程奖励机制，让智能体推理更可信可解释，强化学习新思路。
arXiv:2605.10325v2 Announce Type: replace Abstract: Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of larg…
5
Understanding and Mitigating Premature Confidence for Better LLM Reasoning
最新研究揭示LLM长思维链中“过早自信”导致的逻辑缺口，并提出基于过程奖励模型的缓解策略，提升推理质量。
arXiv:2605.24396v1 Announce Type: new Abstract: Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustif…
6
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…
7
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…
8
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
超越正确性：通过强化学习调和过程与结果奖励，为模型训练提供新视角
arXiv:2509.03403v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves final-answer accuracy on reasoning …