Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models

TL;DR

We identify a transition point (commitment boundary) in LLM chain-of-thought reasoning where the answer is determined, and show that subsequent steps can be omitted without affecting performance, enabling efficient reasoning.

Problem

Chain-of-thought (CoT) reasoning significantly improves LLM reasoning abilities, but it is unclear whether all generated steps causally contribute to the final answer. Unnecessary reasoning steps increase computational cost and latency.

Approach

We use 'early exit' to measure the causal importance of each reasoning step. By observing the change in final answer probability when stopping at a given step, we quantify each step's contribution. This reveals a 'commitment boundary' where the answer suddenly stabilizes. We also show that answer-formation stages can be linearly decoded from intermediate representations using attention probes.

Results & Contribution

Across diverse models and tasks, we confirm the existence of a commitment boundary, after which steps are epiphenomenal (do not affect the final answer). Early exiting at the commitment boundary reduces reasoning length by an average of 55% with negligible performance loss. This provides a practical method to significantly improve the efficiency of CoT reasoning.