RVPO：基于方差正则化的风险敏感对齐
research area Methods and Algorithms, research area Speech and Natural Language Processing
content type paperpublished May 2026
RVPO: Risk-Sensitive Alignment via Variance Regularization
AuthorsIvan Montero, Tomasz Jurczyk, Bhuwan Dhingra
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Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset critical failures in others (e.g., safety or formatting), masking low-performing “bottleneck” rewards vital for reliable multi-objective alignment. We propose Reward-Variance Policy Optimization (RVPO), a risk-sensitive framework that penalizes inter-reward variance during advantage aggregation, shifting the objective from “maximize sum” to “maximize consistency.” We show via Taylor expansion that a LogSumExp (SoftMin) operator effectively acts as a smooth variance penalty. We evaluate RVPO on rubric-based medical and scientific reasoning with up to 17 concurrent LLM-judged reward signals (Qwen2.5-3B/7B/14B) and on tool-calling with rule-based constraints (Qwen2.5-1.5B/3B). By preventing the model from neglecting difficult constraints to exploit easier objectives, RVPO improves overall scores on HealthBench (0.261 vs. 0.215 for GDPO at 14B, p < 0.001) and maintains competitive accuracy on GPQA-Diamond without the late-stage degradation observed in other multi-reward methods, demonstrating that variance regularization mitigates constraint neglect across model scales without sacrificing general capabilities.
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链接抓取：https://arxiv.org/abs/2605.05750
Computer Science > Machine Learning
arXiv:2605.05750 (cs)
[Submitted on 7 May 2026]
Title:RVPO: Risk-Sensitive Alignment via Variance Regularization
Authors:Ivan Montero, Tomasz Jurczyk, Bhuwan Dhingra
View a PDF of the paper titled RVPO: Risk-Sensitive Alignment via Variance Regularization, by Ivan Montero and 2 other authors
Abstract:Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset critical failures in others (e.g., safety or formatting), masking low-performing "bottleneck" rewards vital for reliable multi-objective alignment. We propose Reward-Variance Policy Optimization (RVPO), a risk-sensitive framework that penalizes inter-reward variance during advantage aggregation, shifting the objective from "maximize sum" to "maximize consistency." We show via Taylor expansion that a LogSumExp (SoftMin) operator effectively acts as a smooth variance penalty. We evaluate RVPO on rubric-based medical and scientific reasoning with up to 17 concurrent LLM-judged reward signals (Qwen2.5-3B/7B/14B) and on tool-calling with rule-based constraints (Qwen2.5-1.5B/3B). By preventing the model from neglecting difficult constraints to exploit easier objectives, RVPO improves overall scores on HealthBench (0.261 vs. 0.215 for GDPO at 14B, \(p < 0.001\)) and maintains competitive accuracy on GPQA-Diamond without the late-stage degradation observed in other multi-reward methods, demonstrating that variance regularization mitigates constraint neglect across model scales without sacrificing general capabilities.
| Comments: | 17 pages, 5 figures |
|---|---|
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.05750 [cs.LG] |
| (or arXiv:2605.05750v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05750 Focus to learn more arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ivan Montero [view email]
[v1] Thu, 7 May 2026 06:43:05 UTC (944 KB)
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