Is More Chain‑of‑Thought Always Better? Introducing E‑GRM for On‑Demand LLM Reasoning
The article critically examines the assumption that longer chain‑of‑thought reasoning always improves large language model performance, presents the E‑GRM framework that dynamically decides when to invoke full CoT based on model‑internal uncertainty, and validates its efficiency and accuracy gains through extensive experiments and ablations.
