Adapter Thickets: Splitting an RLVR Budget Beats Concentrating It
Majority voting over sampled completions is the workhorse of test-time scaling, and reinforcement learning with verifiable rewards (RLVR) is the workhorse for making each completion better. The standard pipeline composes the two: train one policy with RLVR, then sample it many times and vote. We show that this composition is lossy. A vote can only overturn mistakes that its voters do not share, and RLVR sharpens a policy so that its samples increasingly make the same mistakes. With every method drawing exactly $160$ completions per problem, training a single LoRA adapter on the full RLVR budget raises single-sample accuracy on every model we test ($1.5$B-$8$B). Yet on three of four models...
| Authors | Jonathan Williams, Esin Tureci, Karthik R. Narasimhan |
|---|---|
| Primary category | cs.LG |
| Published | 1 October 2026 |
| Last updated at source | 2 October 2026 |
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