A step-level reward framework using GPT-4o as a training-time verifier reduces reasoning errors in small language models on physics benchmarks by 10-20% over baselines.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models
A step-level reward framework using GPT-4o as a training-time verifier reduces reasoning errors in small language models on physics benchmarks by 10-20% over baselines.