Semifactual Credit-Augmented Policy Optimization

AI Digest - ArXiv AI

Semifactual Credit-Augmented Policy Optimization

Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions remain sensitive to task-irrelevant prompt features. We investigate this sensitivity through semifactual prompt interventions that preserve the underlying problem and its answer. Our analysis reveals substantial variation in token-level sensitivity and shows that suppressing high-drift token candidates during decoding improves reasoning accuracy wit

Source: ArXiv AI