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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
ThinkPrior, an unreviewed preprint algorithm, uses offline verifier-scored anchor rollouts to initialize a Beta posterior difficulty prior before cold-start RLVR training, reducing early silent groups by over half and wasted rollouts by ~19% through step 30 on a single 7B math model. Final task accuracy was unchanged, and the fixed-budget result represents reallocation rather than absolute savings; this is early-stage work not yet validated across models or domains.
Uncontrolled empirical comparison with random-seed replication. Qwen2.5-Math-7B, a 7-billion-parameter language model, evaluated on a fixed 250-prompt mathematical reasoning pool.. Intervention: ThinkPrior zero-rollout difficulty prior initialized from offline verifier-scored anchor pass; prompt selection by expected learnability with posterior updating from training outcomes.. Compared with: Uniform sampling baseline (standard GRPO); also tested ThinkPrior+DAPO composition..
ThinkPrior more than halves early silent groups compared to uniform sampling baseline Wasted rollouts cut by nearly a fifth (19%) through step 30 No difference detected in final accuracy between ThinkPrior and control
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Unreviewed computational method study showing early-stage algorithmic improvement in rollout efficiency during cold-start prompt selection, with no difference in final task accuracy and limited scope (single model, 250-prompt pool).
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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 before the first target-policy rollout. The verifier-scored anchor pass rate supplies an external-anchor initialization for a Beta posterior; ThinkPrior selects by expected learnability and then updates from training outcomes, changing neither the loss nor the optimizer. On Qwen2.5-Math-7B across sixteen seeds, ThinkPrior more than halves early silent groups and cuts wasted rollouts through step 30 by nearly a fifth, while we detect no difference in final accuracy. On this 250-prompt pool the fixed-budget result is a reallocation rather than a net saving. The measured ThinkPrior+DAPO composition reduces generated rollouts by 10.6% while both arms retain the same 3840-rollout update budget. The prior requires no target-policy rollout before the first selection, but the posterior thereafter uses target-policy outcomes.
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