Diversifying RLVR Rollouts via First-Token Exploration
Reinforcement learning with verifiable rewards (RLVR) trains reasoning models without labeled trajectories, using groups of verifier-scored rollouts to explore alternative reasoning paths. Limited rollout diversity is a central bottleneck, typically addressed through adjustments to temperature, prefixes, or rollout selection. We identify the first token of the response as a structurally distinct target for diversification, largely overlooked in prior work. We find that the first-token distribution is sharply concentrated and only weakly related to downstream correctness, as lower-probability candidates can yield similarly accurate responses. Diversifying the first token can therefore broa...
| Authors | Soeun Kim, Albert No |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 29 September 2026 |
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