Error Typing for Smarter Rewards: Improving Process Reward Models with
  Error-Aware Hierarchical Supervision
Large Language Models (LLMs) are prone to hallucination, especially during
multi-hop and reasoning-intensive tasks such as mathematical problem solving.
While Outcome Reward Models verify only final answers, Process Reward Models
(PRMs) score each intermediate step to steer generation toward coherent
solutions. We introduce PathFinder-PRM, a novel hierarchical, error-aware
discriminative PRM that first classifies math and consistency errors at each
step, then combines these fine-grained signals to estimate step correctness. To
train PathFinder-PRM, we construct a 400K-sample dataset by enriching the
human-annotated PRM800K corpus and RLHFlow Mistral traces with
three-dimensional step-level labels. On PRMBench, PathFinder-PRM achieves a new
state-of-the-art PRMScore of 67.7, outperforming the prior best (65.5) while
using 3 times less data. When applied to reward guided greedy search, our model
yields prm@8 48.3, a +1.5 point gain over the strongest baseline. These results
demonstrate that decoupled error detection and reward estimation not only boost
fine-grained error detection but also substantially improve end-to-end,
reward-guided mathematical reasoning with greater data efficiency.
6835182873a16b09c94ac4d2
Tej Deep Pala
claimed_verified
2025-05-28T10:13:59.973Z
633f875450d83f5065d459d6
/avatars/48312867ac2c05d589869972f90881b9.svg
Pala Tej Deep
user
Tej3
6835182873a16b09c94ac4d3
Panshul Sharma
6835182873a16b09c94ac4d4
Amir Zadeh
6835182873a16b09c94ac4d5
Chuan Li
6835182873a16b09c94ac4d6
Soujanya Poria
admin_assigned
2025-05-27T12:46:25.138Z
626b626405fe1cb65725aca1
https://cdn-avatars.huggingface.co/v1/production/uploads/626b626405fe1cb65725aca1/ZVSbhynzpQhVGq9kGywW6.png
Soujanya Poria
user
soujanyaporia
Error Typing for Smarter Rewards: Improving Process Reward Models with
  Error-Aware Hierarchical Supervision
Large Language Models (LLMs) are prone to hallucination, especially during
multi-hop and reasoning-intensive tasks such as mathematical problem solving.
While Outcome Reward Models verify only final answers, Process Reward Models
(PRMs) score each intermediate step to steer generation toward coherent
solutions. We introduce PathFinder-PRM, a novel hierarchical, error-aware
discriminative PRM that first classifies math and consistency errors at each
step, then combines these fine-grained signals to estimate step correctness. To
train PathFinder-PRM, we construct a 400K-sample dataset by enriching the
human-annotated PRM800K corpus and RLHFlow Mistral traces with
three-dimensional step-level labels. On PRMBench, PathFinder-PRM achieves a new
state-of-the-art PRMScore of 67.7, outperforming the prior best (65.5) while
using 3 times less data. When applied to reward guided greedy search, our model
yields prm@8 48.3, a +1.5 point gain over the strongest baseline. These results
demonstrate that decoupled error detection and reward estimation not only boost
fine-grained error detection but also substantially improve end-to-end,
reward-guided mathematical reasoning with greater data efficiency.
PathFinder-PRM, a hierarchical and error-aware Process Reward Model, improves mathematical problem-solving by fine-grained error classification and step correctness estimation, achieving state-of-the-art PRMScore with reduced data usage.
Large Language Models hallucination mathematical problem solving Outcome Reward Models Process Reward Models PathFinder-PRM hierarchical error-aware discriminative PRM math errors consistency errors step correctness PRMBench PRMScore reward guided greedy search prm@8 data efficiency