# Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning

arXiv: https://arxiv.org/abs/2601.02970 · HTML: https://mlsanity.com/paper/2601.02970/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models](https://mlsanity.com/paper/2410.07985/) (2024)
- [Deep Think with Confidence](https://mlsanity.com/paper/2508.15260/) (2025)
- [Scalable Best-of-N Selection for Large Language Models via Self-Certainty](https://mlsanity.com/paper/2502.18581/) (2025)
- [Confidence Improves Self-Consistency in LLMs](https://mlsanity.com/paper/2502.06233/) (2025)
- [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://mlsanity.com/paper/2401.10480/) (2024)
- [Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus](https://mlsanity.com/paper/2311.13230/) (2023)
- [xVerify: Efficient Answer Verifier for Reasoning Model Evaluations](https://mlsanity.com/paper/2504.10481/) (2025)
- [Soft Self-Consistency Improves Language Model Agents](https://mlsanity.com/paper/2402.13212/) (2024)

## Built on by (most cited first, 11 of 11)

- [Hallucinations Live in Variance](https://mlsanity.com/paper/2601.07058/) (2026)
- [MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling](https://mlsanity.com/paper/2606.12935/) (2026)
- [Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning](https://mlsanity.com/paper/2603.08999/) (2026)
- [Dual-Dimensional Consistency: Balancing Budget and Quality in Adaptive Inference-Time Scaling](https://mlsanity.com/paper/2605.15100/) (2026)
- [Boosting Self-Consistency with Ranking](https://mlsanity.com/paper/2606.05054/) (2026)
- [Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning](https://mlsanity.com/paper/2605.26849/) (2026)
- [Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling](https://mlsanity.com/paper/2609.19499/) (2026)
- [Strategic Self-Consistency](https://mlsanity.com/paper/2609.30352/) (2026)
- [Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models](https://mlsanity.com/paper/2609.35875/) (2026)
- [Certainty Is Not Just Correctness: Rethinking Token-Level Certainty in LLM Reasoning](https://mlsanity.com/paper/2610.00296/) (2026)
- [Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling](https://mlsanity.com/paper/2610.18583/) (2026)

## Abstract (arXiv)

Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by adjusting the sampling budget; however, they rely on count-based stopping rules that treat all responses equally, often leading to unnecessary sampling. We propose Reliability-Aware Adaptive Self-Consistency (ReASC), which addresses this limitation by reframing adaptive sampling from response counting to evidence sufficiency, leveraging response-level confidence for principled information aggregation. ReASC operates in two stages: a single-sample decision stage that resolves instances confidently answerable from a single response, and a reliability-aware accumulation stage that aggregates responses by jointly leveraging their frequency and confidence. Across five models and four datasets, ReASC consistently achieves the best accuracy-cost trade-off compared to existing baselines, yielding improved inference efficiency across model scales from 3B to 27B parameters. As a concrete example, ReASC reduces inference cost by up to 70% relative to self-consistency while preserving accuracy on GSM8K using Gemma-3-4B-it.