Title: One-Token Verification for Reasoning LLMs, Anytime, Anywhere
Primary area: foundation or frontier models, including LLMs
Keywords: LLM, parallel thinking, LoRA, test-time scaling

Abstract: Reasoning large language models (LLMs) have recently achieved breakthrough performance on complex tasks such as mathematical problem solving. A widely used strategy to further improve their performance is parallel thinking, wherein multiple reasoning traces are generated and the final prediction is chosen using methods such as Best-of-N or majority voting. However, two limitations remain: most existing methods lack effective mechanisms for assessing the quality of reasoning traces, typically relying only on final logits or answers, and multi-sample decoding incurs substantial inference latency for long outputs. To address these challenges, we propose **One-Token Verification** (OTV), a lightweight framework for assessing reasoning quality via a single one-token forward during generation. The proposed OTV method introduces a learnable LoRA-based role vector that, without interfering with the primary reasoning process, enables the LLM to assume a verification role and probe the past KV cache for correctness confidence estimation. Unlike generic verifiers or external reward models, OTV is trained natively for each LLM directly leveraging internal computations for token-level scoring. As a result, OTV can provide confidence signals at any point in generation and for any token position, realizing ``anytime, anywhere'' verification. Experiments on math benchmarks show that OTV consistently outperforms state-of-the-art baselines in parallel thinking. Moreover, based on OTV, we introduce efficient variants that terminate most traces early and retain only a single complete reasoning path, reducing the token usage by up to 90\%. In this setting, OTV maintains superior performance while favoring shorter, more reliable solutions.