Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR
For RLVR training of reasoning models, this provides a simple, plug-in method to boost performance without altering existing pipelines.
REFT improves RLVR rollout diversity by uniformly sampling first tokens from the policy's top-N candidates, achieving consistent gains in Pass@1, Pass@8, and Pass@64 over DAPO and GRPO across four base models (0.5B-7B) and three difficulty regimes.
Reinforcement Learning with Verifiable Rewards (RLVR) trains reasoning models without labeled trajectories, relying on grouped rollouts to expose the policy to alternative reasoning paths and a verifier to score them. Rollout diversity has accordingly emerged as a central bottleneck in RLVR, with most existing methods broadening exploration through temperature, prefix, or rollout-selection adjustments. We identify a structurally distinguished but overlooked position for broadening this diversity: the first token after the reasoning marker. The policy's first-token distribution exhibits a sharply peaked yet correctness-decoupled phenomenon, and this first token position can broaden the regions a rollout group covers without altering the correctness signal. We introduce REFT (Rollout Exploration with First-Token Diversification), a light addition to the RLVR pipeline that samples first tokens uniformly from the policy's own top-$N$ candidates and allocates rollouts evenly, leaving every other component unchanged. Trained on the resulting diversified rollouts, REFT improves aggregate Pass@1, Pass@8, and Pass@64 over DAPO and GRPO baselines across four base models (0.5B-7B) and three difficulty regimes.