$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

2026-03-04

AI Summary

This work addresses the challenge of reliably identifying correct answers among multiple candidate solutions in complex reasoning tasks, where existing methods often fail due to their reliance on scalar scoring with independent verification. The authors propose the $V_1$ framework, whose key insight is that pairwise self-verification outperforms independent scoring. Building on this, they introduce an uncertainty-guided tournament ranking algorithm ($V_1$-Infer) and a pairwise reinforcement learning training framework ($V_1$-PairRL), jointly optimizing generation and verification. The approach dynamically allocates verification computation, achieving significant performance gains on code generation and mathematical reasoning benchmarks: up to a 10% improvement in Pass@1 over state-of-the-art test-time scaling methods, and a 7–9% gain in test-time performance with $V_1$-PairRL compared to standard RL, along with an 8.7% absolute Pass@1 improvement and higher computational efficiency.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Test-time scaling for complex reasoning tasks shows that leveraging inference-time compute, by methods such as independently sampling and aggregating multiple solutions, results in significantly better task outcomes. However, a critical bottleneck is verification: sampling is only effective if correct solutions can be reliably identified among candidates. While existing approaches typically evaluate candidates independently via scalar scoring, we demonstrate that models are substantially stronger at pairwise self-verification. Leveraging this insight, we introduce $V_1$, a framework that unifies generation and verification through efficient pairwise ranking. $V_1$ comprises two components: $V_1$-Infer, an uncertainty-guided algorithm using a tournament-based ranking that dynamically allocates self-verification compute to candidate pairs whose relative correctness is most uncertain; and $V_1$-PairRL, an RL framework that jointly trains a single model as both generator and pairwise self-verifier, ensuring the verifier adapts to the generator's evolving distribution. On code generation (LiveCodeBench, CodeContests, SWE-Bench) and math reasoning (AIME, HMMT) benchmarks, $V_1$-Infer improves Pass@1 by up to $10%$ over pointwise verification and outperforms recent test-time scaling methods while being significantly more efficient. Furthermore, $V_1$-PairRL achieves $7$--$9%$ test-time scaling gains over standard RL and pointwise joint training, and improves base Pass@1 by up to 8.7% over standard RL in a code-generation setting.

Problem

• test-time scaling
• complex reasoning
• verification
• candidate selection
• self-verification

Innovation

• pairwise self-verification
• test-time scaling
• uncertainty-guided ranking
• joint generation-verification
• reinforcement learning