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

RHyVE is a new verification and deployment protocol for LLM-generated reward functions in reinforcement learning that addresses a critical gap: when and how to use AI-generated rewards during policy training. The research demonstrates that reward reliability depends on policy competence levels and training phases, requiring adaptive deployment strategies rather than static scheduling.

This research addresses a fundamental challenge in scaling reinforcement learning through language models: generated rewards are not inherently trustworthy training objectives. While prior work focused heavily on reward generation and selection, RHyVE shifts attention to deployment timing—a previously understudied but critical dimension. The protocol uses fork verification to compare reward hypotheses at different policy competence levels, revealing that reward rankings become informative only after task-dependent performance thresholds.

The competence-aware framework emerged from observations that low-competence policies cannot reliably evaluate reward quality, creating a chicken-and-egg problem: you need good rewards to train effective policies, but need effective policies to verify good rewards. RHyVE breaks this cycle through phase-aware deployment that adapts to changing policy capabilities throughout training. The research shows generated reward pools exhibit winner changes across training phases, invalidating universally optimal warm-up schedules.

For the broader AI research community, this work reframes reward generation and deployment as coupled rather than independent problems. The finding that no fixed schedule universally works across different reward candidate families has immediate implications for practitioners building automated reward discovery systems. Experiments with held-out schedule selection and conservative baselines suggest RHyVE functions best as a verification-informed protocol rather than a universal scheduler, limiting overgeneralization claims.

The scope delimitations—dense reward environments and all-failure boundary cases where the method underperforms—establish realistic boundaries for applicability. This methodical approach to characterizing when and where techniques work strengthens the research's contribution to credible AI systems development.

• →Reward hypothesis verification depends critically on policy competence levels, becoming informative only after task-dependent performance thresholds
• →No single training schedule is universally optimal across different generated reward candidate families
• →RHyVE improves peak and retained performance through phase-aware deployment adapted to changing policy capabilities
• →Reward generation and deployment must be studied as coupled problems rather than independent design challenges
• →The method functions as verification-informed deployment protocol with defined scope limitations in dense-reward and failure-boundary scenarios