ThinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR
In reinforcement learning with verifiable rewards (RLVR) trained with group relative policy optimization (GRPO), the KL-free reward-advantage term studied here depends on within-group reward variation. If all rollouts in a group are correct or all are wrong, their group-relative advantages are identically zero; these zero-advantage silent groups provide no reward-advantage gradient, yet uniform sampling spends 39% of a run's rollouts on them. History-based prompt selection must first spend target-policy rollouts to estimate difficulty, creating a cold start with rollout waste; ThinkPrior instead uses an external anchor in one offline pass to construct a zero-rollout difficulty prior befor...
| Authors | Tommy Sha, Skylar Zhai, Siqi Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 11 September 2026 |
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