PAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimization

In three linesPAIR is an internal reward model for multi-step LLM training via GRPO. It combines a hidden-state probe (belief consistency) with a lightweight attention head to generate dense step-level reward signals without external model calls or ground-truth dependencies.

Summary generated by Claude — human-verified