RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses

Problem
This preprint addresses the gap in the literature regarding the reliability of reward hypotheses generated by large language models (LLMs) in reinforcement learning (RL). While prior research has focused on generating and selecting reward candidates, there has been insufficient attention to the verification and deployment of these rewards during policy optimization. The authors highlight that the utility of generated rewards is contingent on the competence of the current policy and the training phase, which has not been adequately explored in existing frameworks.

Method
The authors propose RHyVE, a competence-aware verification and phase-aware deployment protocol. RHyVE operates by comparing small sets of reward hypotheses derived from shared policy checkpoints through a mechanism termed short-horizon fork verification. This method assesses the reliability of reward rankings based on the competence of the policy, which is evaluated against task-dependent thresholds. The authors conduct experiments on a sparse manipulation task, demonstrating that the effectiveness of phase-aware deployment can enhance both peak and retained performance under a locked protocol. The study also investigates the behavior of LLM-generated reward candidates, revealing that the performance of candidate pools can vary based on the phase of training, with no universally optimal warm-up schedule. The authors employ various controls, including held-out schedule selection and conservative selector baselines, to substantiate that RHyVE functions as a verification-informed deployment protocol rather than a one-size-fits-all scheduler.

Results
RHyVE demonstrates significant improvements in performance metrics compared to baseline methods. Specifically, in the sparse manipulation task, phase-aware deployment leads to enhanced peak performance and better retention of performance over time. The results indicate that reward rankings are unreliable at low competence levels but become informative once certain thresholds are surpassed. The experiments reveal that the behavior of generated reward candidates is family-dependent, with variations in performance based on the training phase. The authors provide quantitative comparisons against conservative baselines and compute-matched controls, establishing that RHyVE outperforms these alternatives in terms of effective reward deployment.

Limitations
The authors acknowledge that their approach is limited by the need for accurate competence assessment, which may not always be feasible in practice. Additionally, the experiments are conducted in a specific sparse manipulation context, which may not generalize to all RL tasks. The authors do not address potential scalability issues when applying RHyVE to more complex environments or the computational overhead associated with the verification process.

Why it matters
This work has significant implications for the design and deployment of reward systems in RL, suggesting that reward generation and deployment should be treated as interdependent processes. By emphasizing the importance of competence-aware verification, RHyVE provides a framework for improving the reliability of LLM-generated rewards, which could lead to more effective policy optimization strategies. This research opens avenues for further exploration into adaptive reward systems that can dynamically adjust based on policy performance, potentially enhancing the robustness and efficiency of RL applications.

Authors: Feiyu Wu, Xu Zheng, Zhuocheng Wang, Yi ming Dai, Hui Li
Source: arXiv:2604.28056
URL: https://arxiv.org/abs/2604.28056v1