aiqbench analysis
This research addresses a critical bottleneck in Reinforcement Learning from Human Feedback (RLHF) by proposing distributional reward models as the proper framework for handling uncertainty, thereby mitigating reward hacking. The authors demonstrate how this approach unifies existing heuristic methods like worst-case optimization and mean aggregation under a single closed-form expression derived via Bayesian inference or KL-DRO. For practitioners building alignment pipelines, this work offers a principled theoretical foundation to improve model robustness against proxy errors without relying on ad-hoc ensemble tricks.
A Unifying Lens on Reward Uncertainty in RLHF
arXiv:2606.09073v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) is bottlenecked by reward hacking, where the policy exploits errors in a proxy reward model (RM) and produces high RM scores without genuine quality gains. A natural mitigation is pessimism: lowering rewards in regions where the RM is uncertain. However, standard scalar RMs provide no principled notion of uncertainty. We argue that the right object is a distributional reward model $p(r\mid x,y)$. Under either a Bayesian inference or a KL-distributionally robust optimization (KL-DRO) lens, the KL-regularized RLHF objective admits a closed-form effective reward $\tilde r(x,y) = \pm\beta\log\mathbb{E}_p[e^{\pm r/\beta}]$. The pessimistic branch unifies the prior heuristics for RM ensemble aggregation: mean aggregation, worst-case optimization (WCO), and uncertainty-weighted optimization (UWO) all emerge as limits or truncations of this single expression. This also clarifies the implicit assumptions of each existing rule.
Abstract:Reinforcement learning from human feedback (RLHF) is bottlenecked by reward hacking, where the policy exploits errors in a proxy reward model (RM) and produces high RM scores without genuine quality gains. A natural mitigation is pessimism: lowering rewards in regions where the RM is uncertain. However, standard scalar RMs provide no principled notion of uncertainty. We argue that the right object is a distributional reward model $p(r\mid x,y)$. Under either a Bayesian inference or a KL-distributionally robust optimization (KL-DRO) lens, the KL-regularized RLHF objective admits a closed-form effective reward $\tilde r(x,y) = \pm\beta\log\mathbb{E}_p[e^{\pm r/\beta}]$. The pessimistic branch unifies the prior heuristics for RM ensemble aggregation: mean aggregation, worst-case optimization (WCO), and uncertainty-weighted optimization (UWO) all emerge as limits or truncations of this single expression. This also clarifies the implicit assumptions of each existing rule.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2606.09073 [cs.LG]
(or arXiv:2606.09073v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.09073 Focus to learn more arXiv-issued DOI via DataCite
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