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

Research LLMs & Foundation Models

Merged summary

TL;DR - Off-Context GRPO uses privileged guidance to produce rewarding training rollouts on hard reasoning problems, then importance-corrects updates toward the original unguided objective. It improves mathematical reasoning without meaningful additional cost.

  • Addresses RLVR’s zero-signal failure when models cannot generate any correct solutions.
  • Generates “off-context” rollouts from prompts containing guidance such as solution prefixes.
  • Importance correction avoids the objective mismatch and instability of uncorrected guided training.
  • Achieves a 3.9% absolute average gain over vanilla GRPO across standard math benchmarks, equivalent to a 13.8% relative improvement.

Sources (1)

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

arXiv cs.LG Priyank Agrawal, Ankur Samanta, Shervin Ghasemlou, Jalaj Bhandari, Kavosh Asadi, Daniel Jiang, Aditya Modi 2026-07-21 arXiv:2607.19313

TL;DR - Off-Context GRPO uses privileged guidance to produce rewarding training rollouts on hard reasoning problems, then importance-corrects updates toward the original unguided objective. It improves mathematical reasoning without meaningful additional cost.

  • Addresses RLVR’s zero-signal failure when models cannot generate any correct solutions.
  • Generates “off-context” rollouts from prompts containing guidance such as solution prefixes.
  • Importance correction avoids the objective mismatch and instability of uncorrected guided training.
  • Achieves a 3.9% absolute average gain over vanilla GRPO across standard math benchmarks, equivalent to a 13.8% relative improvement.
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