Study Reveals Self-Consistency Elicitation Confounds Reward Design Effects in Reinforcement Learning
Researchers have shown that a widely used estimand for measuring reward-design effects in reinforcement learning from verifiable rewards (RLVR) conflates two distinct mechanisms, making it systematically biased. The paper introduces a formal telescoping decomposition separating self-consistency elicitation from genuine reward-design signal, validated through a controlled simulator and a pre-registered factorial experiment. This matters because many published RLVR results may be misattributing performance gains to reward design when the effect is actually driven by self-consistency elicitation.
A preprint posted to arXiv argues that the standard naive estimator used to measure reward-design effects in RLVR — computed as the accuracy difference between a true-reward condition and a random-reward condition — is systematically biased because it mixes two separable phenomena: self-consistency elicitation (the tendency of majority pseudo-rewards to sharpen a policy toward its modal answer) and genuine reward-design signal. The authors derive an exact telescoping decomposition (total = null + elicit + rd) and quantify each component across five prior-strength levels using a tabular-GRPO simulator, finding that the reward-design fraction of the naive estimator ranges from only 13.9% at weak priors to 5% at strong priors. A pre-registered 2×2×2 factorial experiment confirms non-additivity between factors, with an interaction ratio of 0.385. The study also distinguishes between regimes that are point-identified versus only partially identified (bounded), depending on proximity to the self-consistency crossover. Re-audits of two previously published results yield starkly different verdicts — one classified as elicitation-dominated (elicitation share 0.98) and one as reward-design-dominated (rd share 1.18) — illustrating the diagnostic utility of the new framework. The authors release a one-command audit harness intended to allow any alignment research team to apply the same decomposition to their own results.
What's missing
The tabular-GRPO simulator used for validation is an abstraction, and it remains an open question how well the decomposition generalizes to large-scale language model training runs with real verifiable-reward tasks. The identities of the two 're-audited' named published results are not disclosed in the abstract, limiting independent verification of those specific verdicts.
What different sources said
- arXiv cs.AICenter
A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR
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