Why Sample What You Can Enumerate? Exact Policy Optimization for Genomic Tool Selection
Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe becomes structurally mismatched in specialist scientific settings where the complete tool-subset space is enumerable. There, a small set of recurring computational capabilities covers the domain, so the space of tool subsets is combinatorial yet small enough to enumerate, and GRPO still estimates an action expectation from a handful of sampled rollouts. Worse, the approximation degrades as training succeeds: as the policy concentrates on preferred subsets it resamples them, sampled rewards collide, and the group-normalized advantage vanish...
| Authors | Haoyue Liu, Xiaoyu Ma, Ye Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 September 2026 |
| Last updated at source | 10 September 2026 |
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