V1 framework unifies generation and verification through efficient pairwise ranking for parallel reasoning.
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.
LLMs are substantially better at comparing two solutions head-to-head than rating single solutions in isolation.
V1-Infer uses a tournament-based pairwise comparison algorithm to improve verification accuracy without quadratic comparisons.
V1-PairRL jointly trains models as both generators and pairwise verifiers, achieving 7-9% test-time scaling gains over pointwise methods.
Pointwise verification suffers from calibration collapse due to lack of globally comparable reference frames for absolute scores.
Self-aggregation methods experience diversity collapse where correct solutions are lost during iterative refinement steps.
Shaobo Wang, Xuan Ouyang +10
As high-quality public text approaches exhaustion, a phenomenon known as the Data Wall, pre-training is shifting from more tokens to better tokens. However, existing methods either rely on heuristic s...
Zhongzhi Li, Xuansheng Wu +3
The diversity of post-training data is critical for effective downstream performance in large language models (LLMs). Many existing approaches to constructing post-training data quantify diversity usi...
Zixuan Huang, Xin Xia +12
Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approach often results in ...
Tengyue Xu, Zhuoyang Qian +17
Autonomous scientific discovery with large language model (LLM)-based agents has recently made substantial progress, demonstrating the ability to automate end-to-end research workflows. However, exist...
Zhiyuan Hu, Yucheng Wang +8
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: poli...
