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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...
Neural combinatorial optimization (NCO) relies on parallel solution sampling for training, yet existing methods fail to fully exploit the rich information latent in a co-sampled solution group. Preference-optimization methods anchor on the single best solution and discard fine-grained quality and structural signal from...
DualCert introduces constraint-coupled learning, in which current degree equations and dynamically separated subtour-elimination constraints (SECs) define each learned transition, and achieves a mean tour-cost gap of 0.0573%, smaller than the reported NeuroLKH mean gap.
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