Process Reward Models › Visualizations

Michael BrenndoerferMarch 10, 20263 min read
Language AI HandbookAll chapters
Part 66 of 67Frontier Methods of 2025
Chapter 501Process Reward Models › Visualizations
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Out[13]:
Visualization
Line plot of PRM training and validation BCE loss versus epoch, both curves decreasing.
Training and validation loss over 9 epochs for the process reward model. Both curves fall quickly and remain close through the validation plateau, indicating the model learns the synthetic step signal without continuing into the later overfitting regime.
Out[14]:
Visualization
Line plot of PRM training and validation accuracy versus epoch, both rising to plateau.
Training and validation accuracy over 9 epochs. Accuracy rises quickly as the PRM learns the main correlation between error-prone embedding regions and step incorrectness, then reaches the validation plateau used to stop training before overfitting.
Out[15]:
Visualization
Grouped bar chart comparing min-step and geometric-mean PRM scores for five candidate solutions.
Comparison of min-step and geometric mean scoring strategies across five candidate solutions. Each pair of bars shows the solution-level score under each strategy. The two strategies frequently disagree on ranking, showing their different tolerances for single-step errors within an otherwise strong solution.
Out[16]:
Visualization
Line plot of PRM step scores across 5 steps for each of 5 candidate solutions with markers.
Step-by-step PRM scores for five candidate solutions. Each line shows how the predicted correctness score evolves across the five steps within a single solution. A steep drop at any step indicates where the model predicts a reasoning error is most likely to occur.