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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 intro...
This work considers the MDP problems with function approximation with function approximation and develops a new algorithm to solve it efficiently, based on a linear programming (LP) reformulation and repeatedly resolves the identified reduced linear system as new transition samples arrive.
An offline model-based RL framework for cost-controllable sequential incentive allocation is developed and an independent counterfactual scorer evaluates each learned policy on held-out logs, enabling pre-launch selection without costly online exposure.
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