KV-PRM：基于 KV-Cache 迁移的高效过程奖励模型，面向多智能体测试时扩展

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Abstract:Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent systems. However, existing PRMs are text-based: they re-encode the entire trajectory text from scratch. In long multi-agent rollouts, the scoring cost, growing quadratically with respect to sequence length L, creates a severe computational bottleneck, severely limiting PRMs' application in long-context scenarios. To resolve this, we introduce KV-PRM, a highly efficient process reward model that eliminates the heavy text re-encoding by directly reading the KV cache produced naturally during the LLM's generation phase. By processing a single "verify token" against the pre-existing KV cache, KV-PRM reduces the scoring cost from O(L^2) to O(L). We formally prove that the KV cache contains strictly greater information capacity than text, and is more efficient for downstream reward modeling. Empirically, across the MATH, GSM8K, and AIME benchmarks, KV-PRM matches or strictly outperforms text-PRMs under various TTS methods such as Beam Search, MCTS, and Weighted Voting, with up to a 5,000x reduction in scoring FLOPs, a 37x reduction in latency, and a 34x reduction in per-sequence memory footprint compared to text-based PRMs.

Comments:	Accepted at NeurIPS 2026. 27 pages, including appendices
Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2607.09153 [cs.AI]
	(or arXiv:2607.09153v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2607.09153 arXiv-issued DOI via DataCite

Submission history

From: Peng Kuang [view email]
[v1] Fri, 10 Jul 2026 07:16:43 UTC (212 KB)
[v2] Wed, 4 Nov 2026 05:26:47 UTC (235 KB)