Risk-Conditioned Fine-Tuning of Large Language Models

Abstract:Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existing Risk-Averse RLHF addresses this issue by optimizing Conditional Value-at-Risk (CVaR), but it trains policies for fixed risk levels and therefore cannot adjust the desired degree of risk aversion at inference time. In this paper, we propose risk-conditioned RLHF, a framework that trains a single policy that provides a continuous risk-control interface, enabling users to select different degrees of risk aversion without retraining or deploying multiple risk-specific models. Experiments across multiple benchmarks demonstrate that a single risk-conditioned policy can adapt to different risk levels at inference time, enabling more flexible and risk-aware LLM deployment.

Machine Learning (cs.LG)

arXiv:2609.08064 [cs.LG]

(or arXiv:2609.08064v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.08064 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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