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arXiv · 2610.09679

CERO: Where and When to Allocate Rollouts for RL Post-Training

Yiming Zong·Yige Wang·Xing Hu·Jiashuo Jiang·Zuo-Jun Max Shen
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Abstract

Adaptive rollout methods for group-relative reinforcement learning typically allocate a fixed per-update budget across prompts. We instead study how to coordinate a finite rollout budget over the entire training horizon. We formulate this problem using a concave surrogate utility of cumulative prompt exposure and introduce CERO, an online primal dual scheduler for prompt admission and budget pacing. In our experiments, each admitted prompt receives a fixed-size response group. CERO instead adapts which prompts are selected, how often they are revisited across rounds, and how many groups are generated in each round. A compact Fenchel representation linearizes the dependence on cumulative exposure, while projected online gradient descent updates prompt-specific supporting slopes and a shared budget price using reward-variation feedback and budget deviations. We establish pathwise guarantees for the surrogate allocation objective against fixed-rate and same-path time-varying benchmarks, with explicit terms for proxy discrepancy and rate variation. Under matched training-response budgets, CERO attains the highest avg@16 macro-average on each of three backbones across five mathematical reasoning benchmarks. Mechanistic analyses link CERO's prompt choices to within-group reward contrast, while multi-seed ablations show gains from adaptive pacing over both uniform and preset spending schedules.

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Yiming Zong, Yige Wang, Xing Hu, Jiashuo Jiang, Zuo-Jun Max Shen. 2026-10-07. CERO: Where and When to Allocate Rollouts for RL Post-Training. https://arxiv.org/abs/2610.09679

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