{"slug": "predicting-inference-time-scaling-gains-from-labeled-validation-set-output", "title": "Predicting Inference-Time Scaling Gains from Labeled Validation-Set Output Statistics", "summary": "Researchers have developed a method to predict how much accuracy improves when using best-of-N inference scaling in language models, without running the full procedure. By analyzing statistics from a single labeled validation-set sampling pass—including agreement spread, first-correct-sample position, and completion-length variance—a compact ridge predictor achieves a Spearman correlation of 0.90 with actual gains. This approach enables efficient screening of candidate configurations before incurring the full cost of reward-model scoring.", "body_md": "arXiv:2606.02981v1 Announce Type: new\nAbstract: Best-of-$N$ inference scaling (drawing $N$ candidate answers from a language model and returning the one a reward model ranks highest) improves accuracy by an amount that varies across models, but predicting that amount in advance currently requires running the procedure end-to-end. Prior work links cheap statistics of a model's sampled outputs and validation-set correctness (how often samples agree, how diverse they are, how confident the model is, and where correct samples appear) to model behavior, but does not isolate which of these form a stable, compact predictor of best-of-$N$ gain. We fit ridge predictors on features computed from a single labeled validation-set sampling pass, use bootstrap-Lasso as a stability analysis of the candidate feature set, and give a concentration analysis with an explicit linear-approximation residual. Across three base-model families, six post-training methods, and math and reasoning task domains, the stability analysis identifies a strict three-feature core spanning prompt-level agreement spread, label-assisted first-correct-sample position, and completion-length variance; a compact ridge predictor built from this core plus an entropy add-on reaches Spearman $\\rho = 0.90$ with actual best-of-$N$ gain under a reward-model verifier. The intended use is labeled validation-set screening of candidate configurations before paying the full reward-model scoring cost.", "url": "https://wpnews.pro/news/predicting-inference-time-scaling-gains-from-labeled-validation-set-output", "canonical_source": "https://arxiv.org/abs/2606.02981", "published_at": "2026-06-03 04:00:00+00:00", "updated_at": "2026-06-03 04:24:03.729833+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/predicting-inference-time-scaling-gains-from-labeled-validation-set-output", "markdown": "https://wpnews.pro/news/predicting-inference-time-scaling-gains-from-labeled-validation-set-output.md", "text": "https://wpnews.pro/news/predicting-inference-time-scaling-gains-from-labeled-validation-set-output.txt", "jsonld": "https://wpnews.pro/news/predicting-inference-time-scaling-gains-from-labeled-validation-set-output.jsonld"}}