Training-free Confident Decoding skips final layers, cuts alignment tax, boosts reasoning
aiamblichus · x · 2026-10-17
An arXiv paper, "Deeper is Not Always Better," reveals a Guess-Refine-Perturb dynamic in LLMs: final layers can perturb refined predictions toward alignment-preferred tokens. Its training-free Confident Decoding dynamically selects the most reliable near-final layer via entropy-guided backward search, framed as an optimal stopping problem. It yields consistent gains on GPQA-Diamond, Omni-MATH and HLE across dense and MoE models, with zero memory overhead and under 2% latency increase. Commenters note the superficial final-layer effect aligns with prior research.
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