Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information

Abstract:Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on difficult problems: when a model cannot generate any correct solutions, it receives \textit{zero} learning signal. Providing privileged guidance during training, such as solution prefixes, can help overcome this learning cliff by steering the model towards {correct solutions with non-zero reward}. {We call these rollouts \textit{off-context}: they are generated from a training prompt that contains privileged guidance, while the target objective is defined by the original prompt without that guidance.} {We introduce} Off-Context GRPO (OC-GRPO), a minimally modified variant of GRPO that uses guided rollouts but applies an importance-corrected objective to steer the update back toward the original unguided objective, avoiding the mismatch that destabilizes uncorrected guided training. Empirically, our algorithm achieves a 3.9\% absolute improvement (13.8\% relative gain) over vanilla GRPO on average across standard mathematical reasoning benchmarks with negligible additional cost.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.19313 [cs.LG]

(or arXiv:2607.19313v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19313 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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