--> Computer Science > Computation and Language arXiv:2602.03584 (cs) [Submitted on 3 Feb 2026 ( v1 ), last revised 31 Mar 2026 (this version, v2)] Title: $V_0$: A Generalist Value Model for Any Policy at State Zero Authors: Yi-Kai Zhang , Zhiyuan Yao , Hongyan Hao , Yueqing Sun , Qi Gu , Hui Su , Xunliang Cai , De-Chuan Zhan , Han-Jia Ye View a PDF of the paper titled $V_0$: A Generalist Value Model for Any Policy at State Zero, by Yi-Kai Zhang and Zhiyuan Yao and Hongyan Hao and Yueqing Sun and Qi Gu and Hui Su and Xunliang Cai and De-Chuan Zhan and Han-Jia Ye View PDF HTML (experimental) Abstract: Policy gradient methods rely on a baseline to measure the relative advantage of an action, ensuring the model reinforces behaviors that outperform its current average capability. In the training of Large Language Models (LLMs) using Actor-Critic methods (e.g., PPO), this baseline is typically estimated by a Value Model (Critic) often as large as the policy model itself. However, as the policy continuously evolves, the value model requires expensive, synchronous incremental training to accurately track the shifting capabilities of the policy. To avoid this overhead, Group Relative Policy Optimization (GRPO) eliminates the coupled value model by using the average reward of a group of rollouts as the baseline; yet, this approach necessitates extensive sampling to maintain estimation stability. In this paper, we propose $V_0$, a Generalist Value Model capable of estimating the expected performance of any model on unseen prompts without requiring parameter updates. We reframe value estimation by treating the policy's dynamic capability as an explicit context input; specifically, we leverage a history of instruction-performance pairs to dynamically profile the model, departing from the traditional paradigm that relies on parameter fitting to perceive capability shifts. Focusing on value estimation at State Zero (i.e., the initial prompt, hence $V_0$), our model serves as a critical resource scheduler. During GRPO training, $V_0$ predicts success rates prior to rollout, allowing for efficient sampling budget allocation; during deployment, it functions as a router, dispatching instructions to the most cost-effective and suitable model. Empirical results demonstrate that $V_0$ significantly outperforms heuristic budget allocation and achieves a Pareto-optimal trade-off between performance and cost in LLM routing tasks. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.03584 [cs.CL] (or arXiv:2602.03584v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.03584 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yi-Kai Zhang [ view email ] [v1] Tue, 3 Feb 2026 14:35:23 UTC (3,933 KB) [v2] Tue, 31 Mar 2026 14:17:21 UTC (3,933 KB) Full-text links: Access Paper: View a PDF of the paper titled $V_0$: A Generalist Value Model for Any Policy at State Zero, by Yi-Kai Zhang and Zhiyuan Yao and Hongyan Hao and Yueqing Sun and Qi Gu and Hui Su and Xunliang Cai and De-Chuan Zhan and Han-Jia Ye View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... 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